Category Archives: Artificial Intelligence
The JD Technology Research Scholarship in Artificial intelligence – University of Sydney
1. Background
a. This Scholarship has been established to provide financial assistance to PhD students who are undertaking fundamental research in Artificial Intelligence.
b. This Scholarship is funded by a collaboration agreement between Jingdong Technology Co Ltd and the University of Sydney.
2. Eligibility
a. The Scholarship is offered subject to the applicant having an unconditional offer of admission to study full-time in a PhD within the Faculty of Engineering at the University of Sydney.
b. Applicants must be willing to conduct fundamental research into Artificial Intelligence and work on an agreed research topic supervised by academic staff in the School of Computer Science at the University of Sydney and research scientists at Jingdong Technology Co Ltd.
c. Applicants must also hold an Honours degree (First Class or Second Class Upper) or equivalent in a relevant discipline.
d. It is a condition of accepting this scholarship that awardees withdraw applications for Research Training Program (RTP) funding, including any RTP Allowance, RTP Fees Offset (international only), RTP Stipend or RTP Scholarships.
e. International students currently outside Australia must have applied for a student visa before they commence their studies.
f. The applicants must be willing to be affiliated with The UBTECH Sydney AI Centre.
3. Selection Criteria
a. The successful applicant will be awarded the Scholarship on the basis of:
I. academic merit, II. curriculum vitae, andIII. area of study and/or research proposal.
b. The successful applicant will be awarded the Scholarship on the nomination of the relevant research supervisor(s), or their nominated delegate(s).
4. Value
a. The Scholarship will provide a stipend allowance equivalent to the University of Sydneys Research Training Program (RTP) Stipend rate (indexed on 1 January each year) for up to 3 years, subject to satisfactory academic performance.
b. The recipient may apply for an extension of the stipend allowance for up to 6 months.
c. The Scholarship will provide $2500 per annum for same duration as the stipend for conference registration fees and conference travel costs to highly ranked conferences. This will be reimbursed to the student upon the provision of receipts through the lead supervisor.
d. Academic course fees and the Student Services Amenities fee (SSAF) are also provided for a successful international applicant, for 12 research periods, subject to satisfactory academic performance.
e. The recipient may apply for an extension of the academic course fees and SSAF for up to 2 research periods.
f. Periods of study already undertaken towards the degree prior to the commencement of the Scholarship will be deducted from the maximum duration of the Scholarship excluding any potential extension period.
g. The Scholarship is for commencement in the relevant research period in which it is offered and cannot be deferred or transferred to another area of research without prior approval.
h. No other amount is payable.
i. The Scholarship will be offered subject to the availability of funding.
5. Eligibility for Progression
a. The Scholarship is maintained by attending and passing the annual progress evaluation, completing 12 credit points of HDR coursework units by the end of year 2 and remaining enrolled in their PhD.
b. Student will be required to participate in the monthly research progress meetings with the principal supervisor from the faculty and the co supervisor from Jingdong Technology Co
6. Leave Arrangements
a. The Scholarship recipient receives up to 20 working days recreation leave each year of the Scholarship and this may be accrued. However, the student will forfeit any unused leave remaining when the Scholarship is terminated or complete. Recreation leave does not attract a leave loading and the supervisor's agreement must be obtained before leave is taken.
b. The Scholarship recipient may take up to 10 working days sick leave each year of the Scholarship and this may be accrued over the tenure of the Scholarship. Students with family responsibilities, caring for sick children or relatives, or experiencing domestic violence, may convert up to five days of their annual sick leave entitlement to carers leave on presentation of medical certificate(s). Students taking sick leave must inform their supervisor as soon as practicable.
7. Research Overseas
a. Scholarship recipients commencing in Australia may not normally conduct research overseas within the first six months of award.
b. Scholarship recipients commencing in Australia may conduct up to 12 months of their research outside Australia. Approval must be sought from the student's lead supervisor, Head of School and the Higher Degree by Research Administration Centre (HDRAC), and will only be granted if the research is essential for completion of the degree. All periods of overseas research are cumulative and will be counted towards a student's candidature. Students must remain enrolled full-time at the University and receive approval to count time away.
c. Scholarship recipients are normally expected to commence their degree in Australia. However, Scholarship recipients who are not able to travel to Australia to start their degree, may commence their studies overseas only if they have applied for their student visa and with the approval of their lead supervisor and Associate Dean (Research Education).
8. Suspension
a. The Scholarship recipient cannot suspend their award within their first six months of study, unless a legislative provision applies.
b. The Scholarship recipient may apply for up to 12 months suspension of the Scholarship for any reason during the tenure of the Scholarship. Periods of Scholarship suspension are cumulative and failure to resume study after suspension will result in the award being terminated. Approval must be sought from the student's supervisor, Jingdong Technology Co Ltd and the Faculty via application to the Higher Degree by Research Administration Centre (HDRAC). Periods of study towards the degree during suspension of the Scholarship will be deducted from the maximum tenure of the Scholarship.
9. Changes in Enrolment
a. The Scholarship recipient must notify HDRAC, Jingdong Technology Co Ltd and their supervisor promptly of any planned changes to their enrolment including but not limited to: attendance pattern, suspension, leave of absence, withdrawal, course transfer, and candidature upgrade or downgrade. If the award holder does not provide notice of the changes identified above, the University may require repayment of any overpaid stipend and tuition fees.
10. Termination
a. The Scholarship will be terminated:
I. on resignation or withdrawal of the recipient from their research degree,II. upon submission of the thesis or at the end of the award,III. if the recipient ceases to be a full-time student and prior approval has not been obtained to hold the Scholarship on a part-time basis, IV. upon the recipient having completed the maximum candidature for their degree as per the University of Sydney (Higher Degree by Research) Rule 2011 Policy,V. if the recipient receives an alternative primary stipend and tuition fees scholarship. In such circumstances this Scholarship will be terminated in favour of the alternative stipend and tuition fees scholarship where it is of higher value, VI. if the recipient does not resume study at the end of a period of approved leave, orVII. if the recipient ceases to meet the eligibility requirements specified for this Scholarship, (other than during a period in which the Scholarship has been suspended or during a period of approved leave). VIII. if the recipient commences their degree from overseas without having applied for their student visa.
b. The Scholarship may also be terminated by the University before this time if, in the opinion of the University:
I. the course of study is not being carried out with competence and diligence or in accordance with the terms of this offer,II. the student fails to maintain satisfactory progress, orIII. the student fails to attend and pass their annual progress evaluation and complete 12 credit points of HDR coursework units by the end of year 2, orIV. the student has committed misconduct or other inappropriate conduct.
c. The Scholarship will be suspended throughout the duration of any enquiry/appeal process.
d. Once the Scholarship has been terminated, it will not be reinstated unless due to University error.
11. Misconduct
a. Where during the Scholarship a student engages in misconduct, or other inappropriate conduct (either during the Scholarship or in connection with the students application and eligibility for the Scholarship), which in the opinion of the University warrants recovery of funds provided, the University may require the student to repay payments made in connection with the Scholarship. Examples of such conduct include and without limitation; academic dishonesty, research misconduct within the meaning of the Research Code of Conduct (for example, plagiarism in proposing, carrying out or reporting the results of research, or failure to declare or manage a serious conflict of interests), breach of the Code of Conduct for Students and misrepresentation in the application materials or other documentation associated with the Scholarship.
b. The University may require such repayment at any time during or after the Scholarship period. In addition, by accepting this Scholarship, the student consents to all aspects of any investigation into misconduct in connection with this Scholarship being disclosed by the University to the funding body and/or any relevant professional body.
12. Intellectual Property
The successful recipient of this Scholarship must complete the Student Deed Poll supplied by the University of Sydney.
13. Acknowledgement
a. The successful applicant must provide Jingdong Technology Co Ltd and the University of Sydney with a copy of any proposed publications within 45 days in advance of submitting for publication. Comments and/or reasonable amendments to the publication can be made by Jingdong Technology Co Ltd and the University of Sydney to protect their Confidential Information and/or Intellectual Property provided they are given to the publishing Party in writing no later than 45 days before the publication is made.
14. Privacy and Confidentiality
a. The successful applicant is required to keep all confidential information disclosed by the Jingdong Technology Co Ltd or the University of Sydney confidential and ensure it is not disclosed to a third party without the prior written consent of the University of Sydney or Jingdong Technology Co Ltd, as appropriate, or as required by law.
b. All information or data provided to the successful recipient by the Jingdong Technology Co Ltd must remain confidential unless used for the purposes of the research outlined in these terms and conditions.
c. The successful recipient must gain written consent from Jingdong Technology Co Ltd prior to use of any confidential information in any thesis or other publication authored by the recipient.
15. Other requirements
a. The successful recipient agrees to provide a copy of their thesis to Jingdong Technology Co Ltd in advance of submitting it for publication.
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The JD Technology Research Scholarship in Artificial intelligence - University of Sydney
How Artificial Intelligence Affects the VPN – Daily Bayonet
Artificial Intelligence helps machines make intelligent decisions. The concern of Artificial Intelligence is to create smarter devices and systems. It helps us process information faster and makes our technology more user-friendly. Artificial Intelligence works by using algorithms to analyze data and find patterns. This allows us to predict outcomes and make better decisions.
To continue this article, we need to answer the next questions: What is VPN, and how does it work? VPNs are secure and encrypted connections that allow you to connect to a remote network. VPNs can be used to access resources such as files, applications, and printers on the remote network. VPNs are also useful for connecting to services when traveling and protecting your privacy when using public Wi-Fi.
How does VPN work? VPNs work by creating a secure connection between your computer and the VPN server. The VPN server is a computer that is located in a different location. All traffic between your computer and the VPN server is encrypted, so your personal information is protected. If you want to learn more about VPNs, you can visit VeePNs website.
Peoples lives have been made easier by artificial Intelligence (AI). The complexity of AI technology has rendered previously complicated challenges obsolete. However, its greatest days are still to come since most of its potential remains unexplored.
Despite the numerous advantages that Artificial Intelligence has provided, there are still drawbacks. At both the professional and personal levels, AI poses security and privacy violations. The encryption technology of VPN in the UAE and other countries might help address these security and privacy violations. On the other hand, the concerns of Artificial Intelligence are higher, and both individuals and corporations must seek a better approach.
Businesses are still dealing with the effects of the Covid-19 pandemics slowdown. In the present climate, all eyes are on AI and VPNs. Both technologies have a lot of room for improvement. Both are expected to evolve.
What is next for these technologies when they evolve further? Continue reading to discover the solution.
Reality Censorship has both positive and harmful aspects. It is based on the concerns of Artificial Intelligence (AI), which can recognize the content and gather data.
Some governments and organizations use it to limit or prohibit access to particular websites. Security and privacy violations, pornographic material, or possibly harmful software might all be factors.
Artificial Intelligence can potentially be abused. These behaviors demonstrate how AI technology can be abused to get an advantage, whether concealing facts or illegally gathering data. The Great Firewall of China is an example of this.
Despite the factors mentioned above, censorship exists today. According to present patterns, it is expected to continue in the future. As long as they are subjected to censorship, users will continue to look for ways to avoid geo-restrictions by using anonymous techniques.
Proxies and virtual private networks (VPNs) are effective choices. Some individuals confuse people by using virtual IP addresses, but knowledgeable users know the differences.
The current needs will drive future developments in the concerns of Artificial Intelligence (AI) technology and Virtual Private Networks (VPN). Because of the significant expenditures made, these technologies will encourage all stakeholders, including corporations, to seek inclusive solutions.
Next-generation VPNs will be the future of VPN technology. These VPNs will mix virtual private network technologies and design. Thanks to cloud technologies, advanced zero trust architecture will be enabled.
New and better capabilities will be accessible in future versions that were not present in previous VPN versions. These features might be entirely new or improvements to existing features, as indicated below.
IP Tunneling: Encryption only covered the data within an IP packet in the past, and it still does today. A VPNs encryption technology will encompass all IP packets in the future.
Faster configuration: Future VPN versions will be simpler and faster to set up than current ones.
Fingerprinting: With this next-generation VPN functionality, customers will be able to identify their activity and data across virtual private networks.
Traffic concealment: VPNs are now used to hide communications that can be easily discovered. As a result, certain VPNs have been blacklisted. Audio and video streaming services are well-known to be restricted. The next-generation VPNs will be difficult to detect by trackers.
These and many more factors will contribute to the future VPN markets complexity with next-generation VPNs. If you are passionate about the changes stated before, you may expect to like them.
Virtual private networks (VPNs) are also benefiting from AI. VPNs for all devices can combat Artificial Intelligence privacy violations using machine learning. According to Smart Data Collective, machine learning algorithms allow VPNs to be more successful in protecting customers from online risks.
Machine learning and virtual private networks work together to take business data security to the next level. Theyre even employed in digital marketing and eCommerce to ensure site security. According to Analytics Insight, machine learning is responsible for helping VPNs attain 90 percent accuracy, according to research published in the Journal of Cyber Security Technology.
AI-based routing is one of the advancements brought about by the concerns of artificial Intelligence. AI-based routing allows internet users to be routed to a VPN server based on their location, connecting to the VPN server closest to their destination.
A user connecting to a website hosted in Japan will most likely receive a Tokyo location as his exit server. Such progress, made possible by Artificial Intelligence, improves ping response while ensuring that VPN traffic stays within the network, making tracking the user considerably more difficult.
VPN also makes home connections more secure, which is important given the rise in online crimes. According to Smart Data Collective, VPN suites leverage artificial intelligence capabilities to offer a whole new degree of security.
AI provides several advantages across the board. It evaluates a large quantity of data, performs several tasks with its algorithms, and connects to the Internet. This is where a virtual private network (VPN) comes into play. Even when used with AI, it helps retain security and privacy violations.
Integrating the functionalities mentioned above in next-generation VPNs will determine the VPN market in the future. Given the contemporary eras rapid technological evolution, you should expect to see these functions sooner.
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How Artificial Intelligence Affects the VPN - Daily Bayonet
As Russia Plots Its Next Move, an AI Listens to the Chatter – WIRED
A radio transmission between several Russian soldiers in Ukraine in early March, captured from an unencrypted channel, reveals panicked and confused comrades retreating after coming under artillery fire.
Vostok, I am Sneg 02. On the highway we have to turn left, fuck, one of the soldiers says in Russian using code names meaning East and Snow 02.
Got it. No need to move further. Switch to defense. Over, another responds.
Later, a third soldier tries to make contact with another codenamed South 95: Yug 95, do you have contact with a senior? Warn him on the highway artillery fire. On the highway artillery fire. Dont go by column. Move carefully.
The third Russian soldier continues, becoming increasingly agitated: Get on the radio. Tell me your situation and the artillery location, approximately what weapon they are firing. Later, the third soldier speaks again: Name your square. Yug 95, answer my questions. Name the name of your square!
As the soldiers spoke, an AI was listening. Their words were automatically captured, transcribed, translated, and analyzed using several artificial intelligence algorithms developed by Primer, a US company that provides AI services for intelligence analysts. While it isnt clear whether Ukrainian troops also intercepted the communication, the use of AI systems to surveil Russias army at scale shows the growing importance of sophisticated open source intelligence in military conflicts.
A number of unsecured Russian transmissions have been posted online, translated, and analyzed on social media. Other sources of data, including smartphone video clips and social media posts, have similarly been scrutinized. But its the use of natural language processing technology to analyze Russian military communications that is especially novel. For the Ukrainian army, making sense of intercepted communications still typically involves human analysts working away in a room somewhere, translating messages and interpreting commands.
The tool developed by Primer also shows how valuable machine learning could become for parsing intelligence information. The past decade has seen significant advances in AIs capabilities around image recognition, speech transcription, translation, and language processing thanks to large neural network algorithms that learn from vast tranches of training data. Off-the-shelf code and APIs that use AI can now transcribe speech, identify faces, and perform other tasks, often with high accuracy. In the face of Russias numerical and artillery advantages, intercepting communications may well be making a difference for Ukrainian troops on the ground.
Primer already sells AI algorithms trained to transcribe and translate phone calls, as well as ones that can pull out key terms or phrases. Sean Gourley, Primers CEO, says the companys engineers modified these tools to carry out four new tasks: To gather audio captured from web feeds that broadcast communications captured using software that emulates radio receiver hardware; to remove noise, including background chatter and music; to transcribe and translate Russian speech; and to highlight key statements relevant to the battlefield situation. In some cases this involved retraining machine learning models to recognize colloquial terms for military vehicles or weapons.
The ability to train and retrain AI models on the fly will become a critical advantage in future wars, says Gourley. He says the company made the tool available to outside parties but refuses to say who. We wont say whos using it or for what theyre using it for, Gourley says. Several other American companies have made technologies, information, and expertise available to Ukraine as it fights against Russian invaders.
The fact that some Russian troops are using unsecured radio channels has surprised military analysts. It seems to point to an under-resourced and under-prepared operation, says Peter W. Singer, a senior fellow at the think tank New America who specializes in modern warfare. Russia used intercepts of open communications to target its foes in past conflicts like Chechnya, so they, of all forces, should have known the risks, Singer says. He adds that these signals could undoubtedly have helped the Ukrainians, although analysis was most likely done manually. It is indicative of comms equipment failures, some arrogance, and possibly, the level of desperation at the higher levels of the Russian military, adds Mick Ryan, a retired Australian general and author.
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As Russia Plots Its Next Move, an AI Listens to the Chatter - WIRED
The Impact of Artificial Intelligence on Law with Chris Garrod, Director of Insurance and Technology at Conyers Dill & Pearman – JD Supra
In this episode of On Record PR, Jennifer Simpson Carr goes on record with Chris Garrod, Director of Insurance and Technology and head of the FinTech group at Conyers Dill & Pearman, to discuss the impact of artificial intelligence on the practice of law.
Chris Garrodis a Director in the Corporate department of Conyers Dill & Pearman. He is a member of the firms insurance practice in Bermuda.
Chris specializes in advising on reinsurance and ILS structures, including large commercial insurers, life reinsurers, special purpose insurers, cat bonds, sidecars and segregated account vehicles. In addition to his insurance practice, he also advises on all aspects of Bermuda corporate law, including mergers and acquisitions, takeovers, reorganizations and re-domestications. He has also formed cryptocurrency vehicles using blockchain-based technology, forming Bermudas first digital token issuer in late 2017. Chris is also a member of the Government of Bermudas Blockchain legal and working group and the Bermuda Business Development Agencys Blockchain working group.
With over 15 years of experience working in Bermudas reinsurance market, Chris acts for a number of large commercial insurers/reinsurers including Assured Guaranty, Chubb, Essent Re, Everest Re, Markel Corporation, Sirius International Insurance Group, Tokio Millennium Re, White Mountains Insurance Group, XL Group, and Zurich Insurance Group.
He has also been involved in the formation of a number of segregated account alternative reinsurance and ILS platforms, acting for reinsurers, investment hedge funds, investment managers, pension plans, investment banks and listed companies. Additionally, he acts for numerous Lloyds syndicates which have Bermuda platforms. Finally, Chris sits on various reinsurance and captive boards in a non-executive capacity advising on Bermuda insurance regulatory and general corporate legal updates to those boards.
Internationally recognized as a leading lawyer, Chris has been recommended in a number of legal directories including Chambers Global and Legal 500, where clients note that his main strengths are his responsiveness and depth of knowledge and that he is very knowledgeable about Bermuda regulations and has a good working relationship with the Bermuda Monetary Authority.
Jennifer Simpson Carr: Welcome to the show, Chris. I often speak about using the power of social media to grow professional networks, and our meeting is certainly a testament to that. I had read an article that you wrote regarding AI and the transformation of law, and we had the opportunity to connect on Twitter and then by email. And, here we are together on the show today.
Chris Garrod: That was probably one of my very first articles. I had started my blog a while back and as a hobby. I think the first one I did was how were all going to rely on robots at some point in the future.
Jennifer Simpson Carr: Im glad to be talking about that to you today because the impact of technology in the legal profession comes up often in our conversations with clients and with referral sources. Before we dive into the discussion:
Im a director at Conyers Dill & Pearman Limited. Were the oldest and the biggest law firm on the island. Ive been practicing there for just over 25 years now in that corporate department. We have offices in Bermuda, BVI and Cayman. We also practice Bermuda, BVI and Cayman law. We have offices in Hong Kong, Singapore and London, and they practice all three laws as well in those offices. I do a mixture of reinsurance and corporate, and Im the head of the firms FinTech group.
Being a FinTech attorney, I must admit that Im a bit of a geek, and thats actually kind of how I got interested in things like artificial intelligence, just meeting people on social media and Twitter in particular and through FinTech. That was how I fell into AI and robotics and the Internet of things and machine learning.
Automation and artificial intelligence sometimes you hear them sort of mixed up in the same sentence and they are very different things. We use automation at Conyers now with automated tasks for legal opinions. In the past when I had to do a legal opinion, it was a question of printing out a document that was manual, marking it up with a pen and paper, and giving it to my assistant. Then, she would have to produce something for me, and Id have to then mark it up again and give it back to her and so on and so forth. It was a long-winded task.
Now, we have a system where lawyers are a lot more computer savvy. Youre entering information into the computer directly, and its very easy to do. Youre just popping in information and then all of a sudden, a document is produced and thats it, everything is completely automated. It requires a lot less reliance on an assistant; its far quicker. It applies to things like simple contracts, simple legal opinions, things in corporations and so on, things which used to require a lot more time now can be done in a more effortless way. Its a time-saving process.
Artificial intelligence, however, is really a layer upon our automation. Its a lot different. It is something which is far deeper. Its machine learning, deep learning. Its actually trying to mimic human intelligence. It is not just me putting information into a computer and its spitting out something. Its actually something along the lines of, Well, I need to get something to replicate what is going in my head. Its the actual knowledge thats kind of going through my mind, but its replicating that in some way, shape or form, in the legal sense, I suppose.
Thats something which were not currently doing right now, very few firms I would say are doing that right now. There is a firm Luminance. Its something that firms can outsource to. They are an AI-powered firm that collect tons of data, and firms will use them to produce contracts and so on. It is an extremely intensive process where loads of information enters their system. But that is an extreme process, which is not just an automated process.
Youre going to see robots one day, but its one level beyond automation. Machine learning is certainly something which I dont think many firms themselves would do internally. Maybe some of the big firms are already doing it, but certainly very few firms are doing it.
Its the input of data and then the output of data related to incorporations or simple contracts or drafting wills. You can go online and work out how to draft a will. Thats the thing, its actually just inputting data and then all of a sudden pressing enter and you will get a certain piece of information. Thats a document that comes out of it. Theres some concern that this will result in the loss of jobs.
Because were suddenly incorporating companies and producing all these legal opinions and were doing contract reviews and all this sort of stuff. As a result, are we cutting out jobs from people who are currently doing this for us? I believe were not going to be doing that.
People used to say, Look, when we have the interaction of robots in factories, for instance, all of the workers who worked in the factories would all of a sudden be made redundant and theyd lose their jobs. But no, in a lot of these industries the actual factory workers themselves ended up keeping their jobs, but they just were retaught and reskilled. They wanted to keep their jobs, and the employers wanted to keep working with these people.
Whats going to probably happen with the legal industry as well is that with artificial intelligence being ramped up more over time and with automation already being introduced, I think were going to see a lot more people being retaught and reskilled.
We dont want to lose employees. Im sure a lot of our employees dont want to leave us either, hopefully. But we can use those people and we want them to stay with us. Its just going to be a matter of reskilling. I think thats going to be the way forward with this industry.
In China theyve been using AI-powered apps for the purposes of courts, judgments, and all sorts of things. In the U.S., theyve been experimenting with artificial intelligence in courtrooms.
I think we shouldnt move too quickly in this space, particularly in artificial intelligence and the legal tech space. I think there are a lot of dangers if we move too quickly.
There are examples regarding things like facial recognition that we need to be mindful of. Because there have been studies where defendants who are minorities or who are non-whites are more likely to be found guilty than white defendants when facial recognition technology is involved.
Thats simply functional because the people who have programmed the actual artificial intelligence are predominantly white males. Whatever you put into this programming is whatever you get out of it. Data is only as unbiased as the heads and hands of its creators. We need to ensure that we are mindful of where were moving, especially in the courtroom.
In my field working as a corporate lawyer, it would be great if I one day have an AI robot to get me a cup of coffee or something along those lines. I would love that to happen in five years time. That is not going to happen anytime soon, but I also dont believe were going to have evil robots either. I dont think there will be evil, legal robots, unless we make evil, legal robots. I would hate to see an evil, legal robot or judge for that matter. Thats actually scary, unless you need an evil litigator. That would be a pretty fun day in the courtroom if that ever happened.
Jennifer Simpson Carr: The seats would be filled.
Chris Garrod: Ill be at the back of the room, thats for sure.
The limitations are, in my view, somewhat obvious. No matter what we think AI can do to help attorneys, the ultimate thing which AI really cant do is replicate empathy, where a human touch is going to be really required. People talk about AI like were going to have killer robots. But whatever you put in is going to be whatever comes out. Whoever is behind the technology is going to be what you get out of it.
As much as I would like to have a robot sitting next to me holding my hand saying, There, there, Chris. Its okay, its okay, its not going to happen for a number of years. But even if it was, it could very well grab my leg and say, This is not going to happen, Chris. Because the person behind it has basically programmed it to grab my leg and say, This is not going to happen, Chris.
You need that level of empathy. A robots not going to be able to replicate that because it has to be programmed. And its not a programmable technology now.
Thats the major limitation we have. I like to think that we always seem to have the human touch to whatever we do right now as lawyers in particular. Because I think its really important.
With the use of automation, were going to do things far more efficiently. Im going to be able to produce a legal opinion or any form of contract in a different format than the way Im doing now. Its just going to be inputting. Im still going to have to review the documents, which Im going to append upon, but the actual production of the contract itself is going to take up far less time. at some point in time.
When we hit a point when AI becomes sophisticated enough that we are producing stuff and clients are getting it, the time spent model will be unsustainable. Were going to have to ask clients, How do you want to be charged for this matter and how can we help you?
The whole point of artificial intelligence is not just to make our own lives easier as lawyers, but also for our clients. How do we make their lives easier from a service perspective? And if were not doing it correctly, then whats the point of even doing it?
I think from a lawyer standpoint, things could change as well. Every new trainee wants to work for one huge law firm. That could change in years to come. You may want to look at working for a more boutique small law firm, which just focuses on one particular aspect of law, because its far more nimble. It can move quickly, it can adapt. And you could also end up with a certain kind of a gig economy. You could have lawyers who basically end up being gig-type attorneys who work when they want and where they want.
There are going to be so many different models in the future, because automation and artificial intelligence opens up all sorts of doors. Im pretty excited about that, whether its in five years time or 10 years time or 50 years time.
Jennifer Simpson Carr: We already see that the generations of young lawyers coming up have grown up with technology. In order for law firms to remain competitive in attracting and retaining top talent, they really do need to evolve their stance on technology. For those of us that didnt get a cell phone until college
Chris Garrod: Or later in life
Jennifer Simpson Carr: Yes, and not a smartphone. It was a simple touch pad of numbers, right? These generations of lawyers coming up grew up with smartphones and grew up with this technology. Law firms will need to evolve and adapt technology in order to remain competitive, not only with their clients compared to what their competitors are doing, but also to retain the talent coming up. Because I do think weve seen the last couple of years that the work model has already changed. I agree with you that it will continue to do so just as these technologies evolve.
I trained two years at what was Denton Hall back then, and now is Dentons, I think its the biggest firm in the world now. The trainees were working as the cogs in a machine. They were paginating, sharing offices with mounds of paper around them. That was one of the reasons I actually came back to Bermuda, because I felt, Well, at least I can get my foot in the door and get started.
But I would say to people who are starting up in this process, keep an open mind. I feel like youre really lucky because this is a brand-new world. Youre entering into the brand-new legal world where you wont be just a cog in a machine. Youre going to have the ability to learn a lot more.
There was a good quote I actually came across on the Luminance website. It was by a partner, Gavin Williams of Holland & Knight. What he said was that AI basically allowed him to spend more time being a lawyer as opposed to being a detective. I thought thats great. I spend a lot of time looking for precedents, looking up things Ive done in the past, looking up all sorts of stuff until I find something.
Ten years from now, youll be able to just be a lawyer and you wont be a cog in this machine, youll be able to actually function as a lawyer. I would say, Look, you wont just be working in a firm which will be a dinosaur. You should hopefully be able to use your training and skills. And you wont just be that detective trying to look for information.
Youll be able to use those skills right away. Im so excited for future generations coming up right now, because theyre very lucky.
Id like to think that well get to a point where lawyers with free time, whether its because theyre not working in huge law firms, whether theyre gig attorneys, or whether theyre working in smaller boutique firms, will be able to help those who are underprivileged those who cant afford or dont have access to legal services and the ability to obtain legal advice.
They will now be able to get access to legal advice because there will be more gig attorneys who will have that flexibility to provide that kind of legal advice, or they will just be attorneys who are working for firms and will simply have more time to provide pro bono legal advice.
I will sometimes give pro bono legal advice on the side, but I dont have loads of time to do that. But as time frees up for more attorneys, they hopefully have more time to provide more pro bono advice to others. Thats something which is just a function of the way we can work. Thats something which we can hopefully encourage in junior attorneys as well as they come up in the system. Thats going to be another a benefit to society as a whole.
Jennifer Simpson Carr: There are so many great opportunities youve touched on, not only for junior attorneys who may be new to the profession, but also service to communities in need. I will drop a link to an interview that we conducted with an attorney who leads the efforts at a law firm for their pro bono support. And I think its a wonderful interview because it talks about not only the benefits to the pro bono clients, but the benefits to the attorneys internally who get the chance to work together, who otherwise arent on the same matters, and how that helps internal collaboration. Chris, thank you so much for joining me today. This has been a lot of fun. Im so glad we got to talk about the potential of robotic litigators in a courtroom. If that happens, were definitely going to have another interview together.
Chris Garrod: Thanks, Jennifer.
Listen to Episode 83: Cultivating Meaningful DE&I: The Positive Impact of Pro Bono with Thomas McHugh of Bressler Amery & Ross
Chris Garrod
Learn more about Chris Garrod
Twitter: @ChrisGGarrod
IRIS Hybrid Speaker offers interactive artificial intelligence and illumination – Yanko Design
Most Italian designers definitely know how to combine form and function. Italians often come up with aesthetically pleasing products that are compatible with the environment and the IRIS Hybrid Speaker is no different.
For the home, we desire new items that are not only powerful but also pleasing to the eyes. Perhaps the easiest to shop for is the entertainment system. We have seen several audio devices already, but well never get tired of checking whats out there. There are a lot of concept designs available, and were hoping some of them will get into production.
Designers: Alessandro Pennese and Alessandro Brintazzoli
The IRIS Hybrid Speaker could be the next music device youd be displaying and using at your home. It fits most interiors that are minimalist yet high-tech. It is designed as an intelligent speaker, ready to offer voice assistance so you can control your other smart home devices or simply make quick conversations.
The IRIS Hybrid Speaker will remind you of donuts because of their shape. There appears to be a paper clip-like handle on the back. The handle allows you to place the speaker almost anywhere.
The speaker portion easily slides on the handle. Thats made possible because of the tracks that allow easier grip and slide. The donut speaker is covered by fabric while a metal bezel surrounds it. The bezel also functions as volume control for the speaker.
The unique design of the IRIS Hybrid Speaker will make you want to display it. You dont have to hide or keep it less noticeable. Instead, you should celebrate and show it off because of the outstanding design that allows it to become an essential part of the home.
An audio device can be part of the living room or any room without overwhelming. The IRIS speaker makes it easier for anyone to decorate the home. It is expected to come with functions that will adequately help in-home space management. It can also be a light source as it functions as a lamp to add to mood lighting and create a more cozy atmosphere.
The IRIS Speaker is imagined to come with interactive artificial intelligence. Its more like smart furniture that offers different functions. Its a fun iteration of a smart speaker technology that provides lighting and works as home decor. The speakers back shell is matte plastic, while the front is fabric. The rear shell doesnt scratch as it can smoothly slide along the tracks. This is to mainly control the LED lights brilliance and
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IRIS Hybrid Speaker offers interactive artificial intelligence and illumination - Yanko Design
Artificial intelligence in auto insurance will give more power to car owners – Automotive News Europe
We are witnessing an exciting time in the automotive industry.
As innovation is reaching new heights with connected and autonomous vehicles, the auto insurance industry is also experiencing its own evolution, using technology to enhance the way road accidents and damages are handled, saving people time and money and improving the often stressful experiences of handling the aftermath of accidents.
A transformation is badly needed in the insurance industry.
In addition to poor customer experiences when making claims, some $25 billion goes unaccounted for each year due to adjuster costs, fraud, delays in repair shops and more. Innovation can change that -- and it is already starting to.
Insurers -- like some car companies now do -- need to think of themselves as technology companies, embracing more AI and data, and also more seamless customer experiences. This is especially importance since insurers will likely have an even deeper relationship with automakers once cars can drive autonomously, at which point the auto company takes responsibility if there is an accident.
These innovations can save time and money while improving transparency, accuracy and efficiency, which are all important to improving the customer experience.
AI and computer vision are among the technologies that can offer the most impact, and their use in growing in insurance. Mobile phone cameras can scan cars after accidents, replacing insurance adjusters in assessing collision damages, making the process faster and more objective.
The next step, now emerging, is using AI and computer vision to automatically generate not just records of damage, but also estimates for how much that damage will cost to repair.
This works by having an expansive database of parts and prices where estimates can be generated when a photo of the damage is uploaded. This puts the power into the hands of the driver or vehicle owner, and takes it out of the repair shops that have for decades relied on the mentality that if the insurance company is paying for it, we can charge as much as we want -- resulting in bloated costs for all parties involved.
If consumers can eventually do this entire process themselves -- not only record damage with their phone cameras, but upload it to their insurance company's platform, and get an instant estimate for the cost of repair -- this would save insurance companies time and money, resulting in lower premiums while empowering the consumer in ways we have yet to see.
Eventually, as cars become more connected and software-based with more sensors, repair costs could be automatically generated by the car's operating system communicating with a digital insurance platform at the time of the car crash. While not available yet, the technology is quickly heading in this direction.
Telematics, which uses GPS devices to track a car's location, distances driven and other factors such as speed, has already led to individual policy pricing. Drivers who demonstrate safer habits can get lower prices on insurance coverage, or, under some plans, just pay for insurance coverage when their car is in use and not while it is sitting in the driveway.
Other data, much of it currently going by the wayside in roadside cameras and sensors, onboard sensors and other IoT devices plus text like accident reports and past claims, promises to add even more information that insurance companies can leverage.
By eventually structuring this data into a usable form that can be analyzed, AI will be even more powerful in determining risk profiles and helping set more exact and personalized policy pricing.
Privacy is also important and insurers will need to make sure to have policies that protect the privacy of their customers who are providing them with an influx of data. In addition to abiding by privacy policies, like GDPR (General Data Protection Regulation), insurers will need to offer benefits, like premium discounts or coupons, to those who share their data.
Now is the time for insurance companies to take advantage of increasing AI and data abilities to prioritize customer experience.
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Artificial intelligence in auto insurance will give more power to car owners - Automotive News Europe
NVIDIA Federal VP Anthony Robbins Selected to 2022 Wash100 for Stewarding New Era of Artificial Intelligence in US Government – ExecutiveBiz
Executive Mosaic is pleased to selectAnthony Robbins, vice president ofNVIDIAs federal arm, as a recipient of the 2022 Wash100 Award, which annually recognizes the top figures in the government contracting community for their impacts across the federal landscape.
This marks Robbins fifth Wash100 selection and represents his commitment to ushering in an era of artificial intelligence-powered transformation within the federal government.
VisitWash100.com to cast your vote for NVIDIAs Anthony Robbins as your favorite GovCon leader and to learn more about the awards nine-year history.
Growth in the artificial intelligence space continues to accelerate unabated, along with the rapidly tightening fabric of relationships that Anthony Robbins of NVIDIA has been weaving throughout the federal sector and government contracting, commentedJim Garrettson, CEO ofExecutive Mosaic and founder of the Wash100 Award. Anthonys prolific social networking efforts and educational thrust toward the marketplace have helped to position NVIDIA as a true pioneer of edge processing, artificial intelligence, cloud enablement and algorithm development. He has played a key role in elevating our national security at the agency and legislative levels.
Garrettson continued, In addition to national defense, homeland security and intelligence, Anthony has helped leverage the colossal strength of NVIDIAs algorithmic solutions to support forest fire mitigation, digital twin modeling in the Omniverse and health care, to name only a few.
Anthony Robbins has long been an advocate for the use of AI in critical government infrastructure and processes. Robbins said the Department of DefensesJoint Artificial Intelligence Center, known as JAIC, aims to tie together disparate AI-related efforts across the public sector to allow for better collaboration and quicker problem-solving capabilities.
He told Federal News Network in February 2021 that JAIC is offering predictive maintenance, consultancy services, and other testing and evaluation services to help the Pentagon scale its AI adoption.
In April 2021, NVIDIA launched its central processing unit,NVIDIA Grace, which was designed to manage high-performance computing and AI workloads at data centers. The U.S. Department of Energys Los Alamos National Laboratory adopted and integrated the NVIDIA Grace CPU into Hewlett Packard Enterprises supercomputing machines for scientific research.
In the same month, NVIDIA announced itsMorpheus cloud-native cybersecurity framework which uses machine learning to detect and prevent cybersecurity attacks in real-time while protecting organizations data centers.
The U.S. Postal service implemented a distributed edge AI system onNVIDIAs EGX platform to help optimize the process of identifying and tracking packages. The platform, known as the Edge Computing Infrastructure Program, was born out of a collaboration between USPS data scientists and NVIDIA architects who teamed up to design deep learning models that could analyze a massive amount of images and packages more efficiently.
The federal government has been for the last several years talking about the importance of artificial intelligence as a strategic imperative to our nation, and as an important funding priority, Robbins said. And this is one of the few enterprise-wide examples of an artificial intelligence deployment that I think can serve to inspire the whole of the federal government.
NVIDIA is also helping the federal government to better predict, mitigate andaddress wildfires using AI and digital twin modeling technology. NVIDIA is working collaboratively with Lockheed Martin to help the Department of Agricultures Forest Service and the Colorado Division of Fire Prevention and Control manage wildfires that pose increasingly frequent and severe threats across the U.S.
Lockheed Martin and NVIDIA will continue to collaborate, both physically and within the digital twin environment, to develop and mature CMM [Cognitive Mission Manager], Robbins said. One day, uncrewed aerial vehicles will rapidly respond to and suppress emerging wildfires.
The NVIDIA-Lockheed partnership also has plans to establish a Silicon Valley-based AI development laboratory to accelerate this effort.
NVIDIA continued its commitment to sustainability and environmental issues through the launch of its platform forscientific digital twins in March 2022. The platform, consisting of NVIDIAs Omniverse and Modulus AI framework, accelerates physics machine learning models in scientific computing and engineering use cases.
Robbins is also an active participant in multiple philanthropic efforts, including the U.S. Marine Corps Toys for Tots program, which collects and distributes Christmas gifts to underprivileged children. He told GovCon Wire in aGovCon Executives Who Care spotlight that the challenges his mother had to overcome in the workforce inspired him to get involved in the program.
As an adult, having been so fortunate to have worked for great companies and being in a community with so much, I am reminded daily were also in a community with many who are perhaps less fortunate, he told GovCon Wire.
I have found that contributing to the program allows me to give back to our community, and perhaps put smiles on the faces and in the hearts of so many wonderful children, Robbins added.
Executive Mosaic congratulates Anthony Robbins and the NVIDIA team on their selection to receive the 2022 Wash100 Award, and we look forward to their continued progress in the federal governments AI-fueled transformation.
Originally posted here:
NVIDIA Federal VP Anthony Robbins Selected to 2022 Wash100 for Stewarding New Era of Artificial Intelligence in US Government - ExecutiveBiz
Six Steps to Responsible AI in the Federal Government – Brookings Institution
There is widespread agreement that responsible artificial intelligence requires principles such as fairness, transparency, privacy, human safety, and explainability. Nearly all ethicists and tech policy advocates stress these factors and push for algorithms that are fair, transparent, safe, and understandable.1
But it is not always clear how to operationalize these broad principles or how to handle situations where there are conflicts between competing goals.2 It is not easy to move from the abstract to the concrete in developing algorithms and sometimes a focus on one goal comes at the detriment of alternative objectives.3
In the criminal justice area, for example, Richard Berk and colleagues argue that there are many kinds of fairness and it is impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness.4 While sobering, that assessment likely is on the mark and therefore must be part of our thinking on ways to resolve these tensions.
Algorithms also can be problematic because they are sensitive to small data shifts. Ke Yang and colleagues note this reality and say designers need to be careful in system development. Worrying, they point out that small changes in the input data or in the ranking methodology may lead to drastic changes in the output, making the result uninformative and easy to manipulate.5
Algorithms also can be problematic because they are sensitive to small data shifts.
In addition, it is hard to improve transparency with digital tools that are inherently complex. Even though the European Union has sought to promote AI transparency, researchers have found limited gains in consumer understanding of algorithms or the factors that guide AI decisionmaking. Even as AI becomes ubiquitous, it remains an indecipherable black box for most individuals.6
In this paper, I discuss ways to operationalize responsible AI in the federal government. I argue there are six steps to responsible implementation:
There need to be codes of conduct that outline major ethical standards, values, and principles. Some principles cut across federal agencies and are common to each one. This includes ideas such as protecting fairness, transparency, privacy, and human safety. Regardless of what a government agency does, it needs to assure that its algorithms are unbiased, transparent, safe, and capable of maintaining the confidentiality of personal records.7
But other parts of codes need to be tailored to particular agency missions and activities. In the domestic area, for example, agencies that work on education and health care must be especially sensitive to the confidentiality of records. There are existing laws and rights that must be upheld and algorithms cannot violate current privacy standards or analyze information in ways that generate unfair or intrusive results.8
In the defense area, agencies have to consider questions related to the conduct of war, how automated technologies are deployed in the field, ways to integrate intelligence analytics into mission performance, and mechanisms for keeping humans in the decisionmaking loop. With facial recognition software, remote sensors, and autonomous weapons systems, there have to be guardrails regarding acceptable versus unacceptable uses.
As an illustration of how this can happen, many countries came together in the 20th century and negotiated agreements outlawing the use of chemical and biological weapons, and the first use of nuclear weapons. There were treaties and agreements that mandated third-party inspections and transparency regarding the number and type of weapons. Even at a time when weapons of mass destruction were pointed at enemies, adversarial countries talked to one another, worked out agreements, and negotiated differences for the safety of humanity.
As the globe moves towards greater and more sophisticated technological innovation, both domestically and in terms of military and national security, leaders must undertake talks that enshrine core principles and develop conduct codes that put those principles into concrete language. Failure to do this risks using AI in ways that are unfair, dangerous, or not very transparent.9
Some municipalities already have enacted procedural safeguards regarding surveillance technologies. Seattle, for example, has enacted a surveillance ordinance that establishes parameters for acceptable uses and mechanisms for the public to report abuses and offer feedback. The law defines relevant technologies that fall under the scope of the law but also illustrates possible pitfalls. In such legislation, it is necessary to define what tools rely upon algorithms and/or machine learning and how to distinguish such technologies from conventional software that analyzes data and acts on that analysis.10 Conduct codes wont be very helpful unless they clearly delineate the scope of their coverage.
Employees need appropriate operational tools that help them safely design and deploy algorithms. Previously, developing an AI application required detailed understanding of technical operations and advanced coding. With high-level applications, there might be more than a million lines of code to instruct processors on how to perform certain tasks. Through these elaborate software packages, it is difficult to track broad principles and how particular programming decisions might create unanticipated consequences.
Employees need appropriate operational tools that help them safely design and deploy algorithms.
But now there are AI templates that bring sophisticated capabilities to people who arent engineers or computer scientists. The advantage of templates is they increase the scope and breadth of applications in a variety of different areas and enable officials without strong technical backgrounds to use AI and robotic process automation in federal agencies.
At the same time, though, it is vital that templates be designed in ways where their operational deployment promotes ethics and fights bias. Ethicists, social scientists, and lawyers need to be integrated into product design so that laypeople have confidence in the use of these tools. There cannot be questions about how these packages operate or on what basis they make decisions. Agency officials have to feel confident that algorithms will make decisions impartially and safely.
Right now, it sometimes is difficult for agency officials to figure out how to assess risk or build emerging technologies into their missions.11 They want to innovate and understand they need to expedite the use of technology in the public sector. But they are not certain whether to develop products in-house or rely on proprietary or open-source software from the commercial market.
One way to deal with this issue is to have procurement systems that help government officials choose products and design systems that work for them. If the deployment is relatively straightforward and resembles processes common in the private sector, commercial products may be perfectly viable as a digital solution. But if there are complexities in terms of mission or design, there may need to be proprietary software designed for that particular mission. In either circumstance, government officials need a procurement process that meets their needs and helps them choose products that work for them.
We also need to keep humans in some types of AI decisionmaking loops so that human oversight can overcome possible deficiencies of automated software. Carnegie Mellon University Professor Maria De-Arteaga and her colleagues suggest that machines can reach false or dangerous conclusions and human review is essential for responsible AI.12
However, University of Michigan Professor Ben Green argues that it is not clear that humans are very effective at overseeing algorithms. Such an approach requires technical expertise that most people lack. Instead, he says there needs to be more research on whether humans are capable of overcoming human-based biases, inconsistencies, and imperfections.13 Unless humans get better at overcoming their own conscious and unconscious biases, manual oversight runs the risk of making bias problems worse.
In addition, operational tools must be human-centered and fit the agency mission. Algorithms that do not align with how government officials function are likely to fail and not achieve their objectives. In the health care area, for example, clinical decisionmaking software that does not fit well with how doctors manage their activities are generally not successful. Research by Qian Yang and her colleagues documents how user-centered design is important for helping physicians use data-driven tools and integrating AI into their decisionmaking.14
Finally, the community and organizational context matter. As argued by Michael Katell and colleagues, some of the most meaningful responsible AI safeguards are based not on technical criteria but on organizational and mission-related factors.15 The operationalization of AI principles needs to be tailored to particular areas in ways that advance agency mission. Algorithms that are not compatible with major goals and key activities are not likely to work well.
To have responsible AI, we need clear evaluation benchmarks and metrics. Both agency and third-party organizations require a means of determining whether algorithms are serving agency missions and delivering outcomes that meet conduct codes.
One virtue of digital systems is they generate a large amount of data that can be analyzed in real-time and used to assess performance. They enable benchmarks that allow agency officials to track performance and assure algorithms are delivering on stated objectives and making decisions in fair and unbiased ways.
To be effective, performance benchmarks should distinguish between substantive and procedural fairness. The former refers to equity in outcomes, while the latter involves the fairness of the process, and many researchers argue that both are essential to fairness. Work by Nina Grgic-Hlaca and colleagues, for example, suggests that procedural fairness needs to consider the input features used in the decision process, and evaluate the moral judgments of humans regarding the use of these features. They use a survey to validate their conclusions and find that procedural fairness may be achieved with little cost to outcome fairness.16
Joshua New and Daniel Castro of the Center for Data Innovation suggest that error analysis can lead to better AI outcomes. They call for three kinds of analysis (manual review, variance analysis, and bias analysis). Comparing actual and planned behavior is important as is identifying cases where systematic errors occur.17 Building those types of assessments into agency benchmarking would help guarantee safe and fair AI.
A way to assure useful benchmarking is through open architecture that enables data sharing and open application programming interfaces (API). Open source software helps others keep track of how AI is performing and data sharing enables third-party organizations to assess performance. APIs are crucial to data exchange because they help with data sharing and integrating information from a variety of different sources. AI often has impact in many areas so it is vital to compile and analyze data from several domains so that its full impact can be evaluated.
Technical standards represent a way for skilled professionals to agree on common specifications that guide product development. Rather than having each organization develop its own technology safeguards, which could lead to idiosyncratic or inconsistent designs, there can be common solutions to well-known problems of safety and privacy protection. Once academic and industry experts agree on technical standards, it becomes easy to design products around those standards and safeguard common values.
An area that would benefit from having technical standards is fairness and equity. One of the complications of many AI algorithms is the difficulty of measuring fairness. As an illustration, fair housing laws prohibit financial officials from making loan decisions based on race, gender, and marital status in their assessments.
One of the complications of many AI algorithms is the difficulty of measuring fairness.
Yet AI designers either inadvertently or intentionally can find proxies that approximate these characteristics and therefore allow the incorporation of information about protected categories without the explicit use of demographic background.18
AI experts need technical standards that guard against unfair outcomes and proxy factors that allow back-door consideration of protected characteristics. It does not help to have AI applications that indirectly enable discrimination by identifying qualities associated with race or gender and incorporating them in algorithmic decisions. Making sure this does not happen should be a high priority for system designers.
Pilot projects and organizational sandboxes represent ways for agency personnel to experiment with AI deployments without great risk or subjecting large numbers of people to possible harm. Small scale projects that can be scaled up when preliminary tests go well protect AI designers from catastrophic failures while still offering opportunities to deploy the latest algorithms.
Federal agencies typically go through several review stages before launching pilot projects. According to Dillon Reisman and colleagues at AI Now, there are pre-acquisition reviews, initial agency disclosures, comment periods, and due process challenges periods. Throughout these reviews, there should be regular public notices so vendors know the status of the project. In addition, there should be careful attention to due process and disparate analysis impact.
As part of experimentation, there needs to be rigorous assessment. Reisman recommends opportunities for researchers and auditors to review systems once they are deployed.19 By building assessment into design and deployment, it maximizes the chance to mitigate harms before they reach a wide scale.
The key to successful AI operationalization is a well-trained workforce where people have a mix of technical and nontechnical skills. AI impact can range so broadly that agencies require lawyers, social scientists, policy experts, ethicists, and system designers in order to assess all its ramifications. No single type of expertise will be sufficient for the operationalization of responsible AI.
For that reason, agency executives need to provide funded options for professional development so that employees gain the skills required for emerging technologies.20 As noted in my previous work, there are professional development opportunities through four-year colleges and universities, community colleges, private sector training, certificate programs, and online courses, and each plays a valuable role in workforce development.21
Federal agencies should take these responsibilities seriously because it will be hard for them to innovate and advance unless they have a workforce whose training is commensurate with technology innovation and agency mission. Employees have to stay abreast of important developments and learn how to implement technological applications in their particular divisions.
Technology is an area where breadth of expertise is as important as depth. We are used to allowing technical people to make most of the major decisions in regard to computer software. Yet with AI, it is important to have access to a diverse set of skills, including those of a non-technical nature. A Data and Society article recommended that it is crucial to invite a broad and diverse range of participants into a consensus-based process for arranging its constitutive components. 22 Without access to individuals with societal and ethical expertise, it will be impossible to implement responsible AI.
Thanks to James Seddon for his outstanding research assistance on this project.
The Brookings Institution is a nonprofit organization devoted to independent research and policy solutions. Its mission is to conduct high-quality, independent research and, based on that research, to provide innovative, practical recommendations for policymakers and the public. The conclusions and recommendations of any Brookings publication are solely those of its author(s), and do not reflect the views of the Institution, its management, or its other scholars.
Microsoft provides support to The Brookings InstitutionsArtificial Intelligence and Emerging Technology (AIET) Initiative. The findings, interpretations, and conclusions in this report are not influenced by any donation. Brookings recognizes that the value it provides is in its absolute commitment to quality, independence, and impact. Activities supported by its donors reflect this commitment.
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Six Steps to Responsible AI in the Federal Government - Brookings Institution
Companies In The Artificial Intelligence In Healthcare Market Are Introducing AI-Powered Surgical Robots To Improve Precision As Per The Business…
LONDON, March 30, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Companys research report on the artificial intelligence in healthcare market, AI-driven surgical robots are gaining prominence among the artificial intelligence in healthcare market trends. Various healthcare fields have adopted robotic surgery in recent times. Robot-assisted surgeries are performed to remove limitations during minimally invasive surgical procedures and to improve surgeons' capabilities during open surgeries. AI is widely being applied in surgical robots and is also used with machine vision to analyze scans and detect complex cases. While performing surgeries in delicate areas of the human body, robotic surgeries are more effective than manually performed surgeries. To meet healthcare needs, many technology companies are providing innovative robotic solutions.
For example, in 2020, Accuracy Incorporated, a US-based company that develops, manufactures, and sells radiotherapy systems for alternative cancer treatments, launched a device called the CyberKnife S7 System, which combines speed, advanced precision, and AI-driven motion tracking for stereotactic radiosurgery and stereotactic body radiation therapy treatment.
Request for a sample of the global artificial intelligence in healthcare market report
The global artificial intelligence in healthcare market size is expected to grow from $8.19 billion in 2021 to $10.11 billion in 2022 at a compound annual growth rate (CAGR) of 23.46%. The global AI in healthcare market size is expected to grow to $49.10 billion in 2026 at a CAGR of 48.44%.
The increase in the adoption of precision medicine is one of the driving factors of artificial intelligence in the healthcare market. Precision medicine uses information about an individual's genes, environmental and lifestyle changes to design and improve the diagnosis andtherapeutics of the patient. It is widely used for oncology cases, and due to the rising prevalence of cancer and the number of people affected by it, the demand for AI in precision medicine will increase. According to research published in the Lancet Oncology, the global cancer burden is set to increase by 75% by 2030.
Major players in the artificial intelligence in healthcare market are Intel Corporation, Nvidia Corporation, IBM Corporation, Microsoft Corporation, Google Inc., Welltok Inc., General Vision Inc., General Electric Company, Siemens Healthcare Private Limited, Medtronic, Koninklijke Philips N.V., Micron Technology Inc., Johnson & Johnson Services Inc., Next IT Corporation, and Amazon Web Services.
The global artificial intelligence in healthcare market is segmented by offering into hardware, software; by algorithm into deep learning, querying method, natural language processing, context aware processing; by application into robot-assisted surgery, virtual nursing assistant, administrative workflow assistance, fraud detection, dosage error reduction, clinical trial participant identifier, preliminary diagnosis; by end-user into hospitals and diagnostic centers, pharmaceutical and biopharmaceutical companies, healthcare payers, patients.
As per the artificial intelligence in healthcare industry growth analysis, North America was the largest region in the market in 2021. Asia-Pacific is expected to be the fastest-growing region in the global artificial intelligence in healthcare market during the forecast period. The regions covered in the global artificial intelligence in healthcare market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa.
Artificial Intelligence In Healthcare Market Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide artificial intelligence in healthcare market overviews, analyze and forecast market size and growth for the whole market,artificial intelligence in healthcare market segments and geographies, artificial intelligence in healthcare market trends, artificial intelligence in healthcare market drivers, artificial intelligence in healthcare market restraints,artificial intelligence in healthcare market leading competitors revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.
The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors approaches.
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Robotic Surgery Devices Global Market Report 2022 By Product And Service (Robotic Systems, Instruments & Accessories, Services), By Surgery Type (Urological Surgery, Gynecological Surgery, Orthopedic Surgery, Neurosurgery, Other Surgery Types), By End User (Hospitals, Ambulatory Surgery Centers) Market Size, Trends, And Global Forecast 2022-2026
Artificial Intelligence (AI) In Drug Discovery Global Market Report 2022 By Technology (Deep Learning, Machine Learning), By Drug Type (Small Molecule, Large Molecules), By Therapeutic Type (Metabolic Disease, Cardiovascular Disease, Oncology, Neurodegenerative Diseases), By End-Users (Pharmaceutical Companies, Biopharmaceutical Companies, Academic And Research Institutes) Market Size, Trends, And Global Forecast 2022-2026
Precision Medicine Global Market Report 2022 By Technology (Big Data Analytics, Bioinformatics, Gene Sequencing, Drug Discovery, Companion Diagnostics), By Application (Oncology, Respiratory Diseases, Central Nervous System Disorders, Immunology, Genetic Diseases), By End-User ( Hospitals And Clinics, Pharmaceuticals, Diagnostic Companies, Healthcare And IT Firms) Market Size, Trends, And Global Forecast 2022-2026
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Companies In The Artificial Intelligence In Healthcare Market Are Introducing AI-Powered Surgical Robots To Improve Precision As Per The Business...
Artificial Intelligence in Cybersecurity Market Worth $66.22 billion by 2029 — Exclusive Report by Meticulous Research – PR Newswire
REDDING, Calif., March 29, 2022 /PRNewswire/ -- According to a new market research report titled, "Artificial Intelligence in Cybersecurity Marketby Technology (ML, NLP), Security (Endpoint, Cloud, Network), Application (DLP, UTM, IAM, Antivirus, IDP), Industry (Retail, Government, BFSI, IT, Healthcare), and Geography Global Forecasts to 2029,"the artificial intelligence in cybersecurity market is expected to grow at a CAGR of 24.2% during the forecast period to reach $66.22 billion by 2029.
DownloadFree Sample Report Now @https://www.meticulousresearch.com/download-sample-report/cp_id=5101
The increasing demand for advanced cybersecurity solutions and privacy, the growing significance of AI-based cybersecurity solutions in the banking sector, and the rising frequency and complexity of cyber threats are the key factors driving the growth of the artificial intelligence in cybersecurity market. In addition, the growing need for AI-based cybersecurity solutions among small and medium-sized enterprises (SMEs) are creating new growth opportunities for vendors in the AI in cybersecurity market.
However, the lack of skilled AI professionals, the perception of AI in cybersecurity as an uncomprehensive security solution, and the impacts of the COVID-19 pandemic are expected to restrain the growth of this market to a notable extent.
Impact of COVID-19 on Artificial Intelligence in CybersecurityMarket
The outbreak of the COVID-19 pandemic severely affected numerous businesses across the globe. Several organizations started adopting a work-from-home mode of operation. Reliance on remote access systems made organizations more vulnerable to DDOS and phishing attacks. The excess internet usage during the COVID-19 pandemic increased the number of data thefts, ransomware attacks, and data breaches across organizations. The COVID-19 outbreak forced medical-treatment units to utilize remote-care devices that lack proper protection against cyberattacks, thereby giving opportunities to perpetrate attacks. Thus, several organizations started focusing on advanced technologies in cybersecurity, such as artificial intelligence, machine learning, and big data, for protection against such threats.
According to HIPAA Journal, the INTERPOL issued an alert to hospitals over ongoing ransomware attacks during the COVID-19 pandemic and issued a 'Purple Notice' alerting police forces in all 194 member countries. According to Cyber Intelligence Centre, in 2020, there was a spike of phishing attacks, malspams, and ransomware attacks as attackers started using COVID-19 as bait to personate brands and mislead customers and employees. Organizations started focusing on monitoring emails and active directories for anomalous logins, reviewing tactical actions, and implementing key security controls. Many leading players started improving their cyber risk management measures strategizing accordingly by ensuring that their remote access systems were sufficiently resilient to withstand cyber threats. Artificial intelligence in cybersecurity will play a significant role in the safety and security of organizations from cyber threats. The impacts of the COVID-19 pandemic have resulted in industry verticals focusing on reducing reliance on human resources and maximizing the use of processes and advanced technologies to perform cybersecurity activities. The positive impacts will be perceptible, encouraging the growth of AI in cybersecurity market during the forecast period.
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Several organizations are eying this crisis as a new opportunity for restructuring and revisiting their existing strategies along with advanced product portfolios.For instance, in 2021, IBM Corporation (U.S.) announced new and enhanced services of IBM Security Services designed to help organizations manage their cloud security strategy, policies, and controls across hybrid cloud environments. In 2021, FireEye, Inc. introduced FireEye XDR, a unified platform designed to help security operations teams strengthen threat detection, accelerate response capabilities, and simplify investigations. These developments are showing a positivesign for the growth of cybersecurity, consequently expected to boost the demand for artificial intelligence in cybersecurity market in the coming years.
In addition, favorable government policy & initiatives, including financial packages for businesses and ease in taxes, the increasing demand for advanced cybersecurity solutions in the healthcare sector, and the increasing investments in advanced technologies are expected to contribute to the growth of artificial intelligence in cybersecurity market.
Also, the ongoing tensions between Russia and Ukraine have increased digital skirmishes. This ongoing conflict may impact global diplomacy, markets, and other aspects by escalating into cyberwarfare with significant monetary damages, as seen with the global NotPetya cyberattack and wiper virus cyberattack.
Artificial Intelligence in Cybersecurity Market Overview
The artificial intelligence in cybersecurity market is segmented based on components (hardware, software, services), technology (machine learning, natural language processing, context-aware computing), security (application security, endpoint security, cloud security, network security), application (data loss prevention, unified threat management, encryption, identity & access management, risk & compliance management, antivirus/antimalware, intrusion detection/prevention system, distributed denial of service mitigation, security information & event management, threat intelligence, fraud detection), deployment (on-premises, cloud-based), industry vertical (retail, government & defense, automotive & transportation, BFSI, manufacturing, infrastructure, IT & telecommunication, healthcare, aerospace, education, energy), and geography. The study also evaluates industry competitors and analyses the market at the country level.
Based on component, the AI in cybersecurity market is segmented into software, hardware, and services. In 2022, the software segment is estimated to account for the largest share of the overall artificial intelligence in cybersecurity market. The larger share and highest CAGR of this segment is primarily driven by the growing data security concerns, the increase in demand for AI platforms solutions for security operations, and the surge in demand for robust and cost-effective security solutions among business enterprises to strengthen their cybersecurity infrastructure.
Based on technology, the overall AI in cybersecurity market is segmented into machine learning, natural language processing (NLP), and context-aware computing. In 2022, the machine learning technology segment is estimated to account for the largest share of the overall artificial intelligence in cybersecurity market.The large share and highest CAGR of this segment is primarily attributed to its advanced ability to collect, process, and handle big data from different sources that offer rapid analysis and prediction. It also helps analyze user behavior and learnfrom them to help prevent attacks and respond to changing behavior. In addition, it helps find threats and respond to active attacks in real-time, reduces the amount of time spent on routine tasks, and enables organizations to use their resources more strategically, further supporting the growth of the machine learning technology market in the coming years.
Based on security, the AI in cybersecurity market is segmented into network security, cloud security, endpoint security, and application security. In 2022, the network security segment is estimated to account for the largest share of the overall artificial intelligence in cybersecurity market.The large share of this segment is mainly attributed to the adoption of the Bring Your Own Device (BYOD) trend, the increasing number of APTs, malware, and phishing attacks, the increasing need for secure data transmission, the growing demand for network security solutions, and rising privacy concerns. However, the cloud security segment is slated to register the highest CAGR during the forecast period due to the increased adoption of Internet of Things (IoT) devices, surge in the deployment of cloud solutions, the emergence of remote work and collaboration, and the increasing demand for robust and cost-effective security services.
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Based on application, this market is segmented into data loss prevention, unified threat management, encryption, identity & access management, risk & compliance management, intrusion detection/prevention system, antivirus/antimalware, distributed denial of service (DDoS) mitigation, Security Information and event management (SIEM), threat intelligence, and fraud detection. In 2022, the identity and access management segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the increase in security concerns among organizations, the increasing number and complexity of cyber-attacks, the growing need for integrity & safety of confidential information in industry verticals, and the growing emphasis on compliance management. However, the data loss prevention segment is slated to register the highest CAGR during the forecast period due to the increasing regulatory and compliance requirements and the growing need to address data-related threats, including the risks of accidental data loss and exposure of sensitive data in organizations.
Based on industry vertical, the AI in cybersecurity market is segmented into government & defense, retail, manufacturing, banking, financial services, and insurance (BFSI), automotive & transportation, healthcare, IT & telecommunication, aerospace, education, and energy. In 2022, the IT & telecommunication sector is estimated to account for the largest share of the overall AI in cybersecurity market. The large share of this segment is mainly attributed to increasing incidence of security breaches by cybercriminal, shifting preference from traditional business models to sophisticated technologies, and including IoT devices, 5G, and cloud computing.However, the healthcare sector is slated to register the highest CAGR during the forecast period due to the rising sophistication levels of cyber-attacks, the growing incorporation of advanced cybersecurity solutions, the exponential rise in healthcare data breaches, and the growing adoption of IoT & connected devices across the healthcare sector.
Based on deployment, the market is segmented into on-premises and cloud-based. In 2022, the on-premises segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the increasing necessity for enhancing the internal processes & systems, security issues related to cloud-based deployments, and the rising demand for advanced security application software among industry verticals. However, the cloud-based segment is slated to register the highest CAGR during the forecast period due to the increasing number of large enterprises using cloud platforms for data repositories and the growing demand to reduce the capital investment required to implement cybersecurity solutions. In addition, several organizations are moving operations to the cloud, leading cybersecurity vendors to develop cloud-based solutions.
Based on geography, in 2022, North America is estimated to account for the largest share of the overall artificial intelligence in cybersecurity market. The large market share of North America is attributed to the presence of major players along with several emerging startups in the region, the increase in government initiatives towards advanced technologies, such as artificial intelligence, the proliferation of cloud-based solutions, the increasing sophistication in cyber-attacks, and the emergence of disruptive digital technologies. However, Asia-Pacific is expected to register the highest CAGR during the forecast period. Factors such as the rising number of connected devices, the increasing privacy & security concerns, the growing awareness regarding cybersecurity among organizations, rapid economic development, high adoption of advanced technologies, such as IoT, 5G technology, and cloud computing are contributing to the growth of this market in Asia-Pacific.
The global artificial intelligence in cybersecurity market is fragmented in nature. The major players operating in this market are Amazon Web Services, Inc. (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), FireEye, Inc. (U.S.), Palo Alto Networks, Inc. (U.S.), Juniper Networks, Inc. (U.S.), Fortinet, Inc. (U.S.), Cisco Systems, Inc. (U.S.), Micron Technology, Inc. (U.S.), Check Point Software Technologies Ltd. (U.S.), Imperva (U.S.), McAfee LLC (U.S.), LogRhythm, Inc. (U.S.), Sophos Ltd. (U.S.), NortonLifeLock Inc. (U.S.), and Crowdstrike Holdings, Inc. (U.S.)among others.
Browse in-depth TOC on"Artificial Intelligence In Cybersecurity Market - Global Opportunity Analysis And Industry Forecast (2022-2029)"380 Tables43 Figures441 Pagesclick here:https://www.meticulousresearch.com/product/artificial-intelligence-in-cybersecurity-market-5101
Scope of the Report:
AI in Cybersecurity Market by Component
AI in Cybersecurity Market by Technology
AI in Cybersecurity Market by Security Type
AI in Cybersecurity Market by Application
AI in Cybersecurity Market by Deployment Type
AI in Cybersecurity Market by Industry Vertical
AI in Cybersecurity Market by Geography:
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Artificial Intelligence in Cybersecurity Market Worth $66.22 billion by 2029 -- Exclusive Report by Meticulous Research - PR Newswire