Category Archives: Artificial Intelligence
The influence of artificial intelligence on the current trends of material science – Economic Times
The recent years have experienced a burgeoning growth in the development of statistical and machine learning within the domains of materials science and polymer chemistry. Interestingly, or rather unnoticeably, the concept of artificial intelligence was prevalent in the material science community for the past couple of decades. For instance, more than 15 years ago, a symposium proceeding conducted by the Materials Research Society had a session titled Combinatorial and Artificial Intelligence Methods in Materials Science. The trend has evolved recently with contemporary topics like high throughput screening, particle simulation accelerator, and using computational data sets to develop ground states.
The first question I asked myself is, why is this field proliferating now? Furthermore, if the area had been into practice 15 years ago, what happened to the techniques since then? Well, this somewhat resembles the rise and fall of the artificial intelligence, which generally has the crest and the trough, commonly termed as the resurgence and AI winters respectively.
The first spark was seen in 1956, when the context of artificial intelligence was created. Back then, the scientist didnt know how to deal with the computational science. Moreover, there was no proper bridge that could link the experimental data with the theoretical data obtained from computational programming. The domain became more reinforced during the 1980s with the advent of powerful algorithms like backpropagation (for neural networks) and kernel methods (for classification). Now, with the integration of deep learning along with the growth in graphics processing units, the computational techniques have opened up a lot of avenues in the field of material sciences.
But, is the current technique enough to bridge the distance between the materials and the scientific community?
I guess, yes. The primary element which determines the robustness of an artificial intelligence processing and operation is the availability of large volumes of arranged data, which the literature terms as libraries. These libraries enable us to use the machine learning fundamentals, but at the same time provide the scope to interpret them physically.
If harmonized and processed precisely, artificial intelligence not only allows us to accelerate our scientific developments but also the way particular research can be conducted. That is why you will find various recent articles that focus on ways to develop quicker routes to perform the same contemporary experiments. In this context, the Materials Genome Initiative, which was launched in 2011, had the sole intention to accelerate the material discovery process and to scale them up. The primary steps they used to establish the above goals were to apply the high throughput algorithm, both the theoretical and experimental modeling, to develop accessible libraries and repositories. Since then, the datasets have become a traditional solution to deal with complex problems in material sciences. The course of evolution eventually developed various datasets that contain thousands of experimental and theoretical data points including the Automatic Flow for Materials Discovery (AFLOWLIB), Joint Automated Repository for Various Integrated Simulations (JARVIS), density functional theory (DFT)), Polymer Genome, Citrination, and Materials Innovation Network.
The question remains- how exactly do these advanced techniques help us to develop a new perspective in material sciences? Well, let me give you an elementary example. Let say; I have developed a robust library with machine learning which hosts data for alloy designing. Once I know what kind of alloy to fabricate, I can set the parameters in the library to find the most optimized set of materials and operation tools which can fetch me the desired results in the least required time. Can we do the same using experimental and pure theoretical techniques? No, since most of the time shall be consumed while conducting trails from the vast set of the data. Moreover, these libraries can be extended to accelerate the synthesis optimization process, along with integrating train models to classify the crystal structures and defects. The most recent application involves the development of various de novo molecules for reinforced molecular designs for identifying materials with specific properties desired for various sensible operations.
As a concluding note, the availability of such databases and amalgamating them with theoretical and machine learning methods offer the potential to alter how materials science is approached substantially.
DISCLAIMER : Views expressed above are the author's own.
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The influence of artificial intelligence on the current trends of material science - Economic Times
Insights into the North America Artificial Intelligence in Fashion Market to 2027 – Drivers, Restraints, Opportunities and Trends -…
DUBLIN--(BUSINESS WIRE)--The "North America Artificial Intelligence in Fashion Market to 2027 - Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User Industry" report has been added to ResearchAndMarkets.com's offering.
The North America artificial intelligence in fashion market accounted for US$ 128.7 Mn in 2018 and is expected to grow at a CAGR of 37.9% over the forecast period 2019-2027, to account for US$ 2254.2 Mn in 2027.
The artificial intelligence in fashion market is fragmented in nature due to the presence of several end-user industries, and the competitive dynamics in the market are anticipated to change during the coming years. In addition to this, various initiatives are undertaken by governmental bodies to accelerate the artificial intelligence in fashion market further. The North American countries are developing various policies and outlining best practices to implement artificial intelligence for promoting innovation in various industry sectors.
Further, the political agendas for North American countries are aligned with the development of Machine Learning (ML) and Artificial Intelligence (AI). Artificial intelligence technologies such as self-adapting machine learning, deep learning or Natural language processing are expected to transform the way businesses work. Governments of various North American countries are working on drafting robust and comprehensive set of regulations and policies for a holistic development of artificial intelligence in this region.
Reasons to Buy
Key Topics Covered:
1. Introduction
2. Key Takeaways
3. Research Methodology
3.1 Coverage
3.2 Secondary Research
3.3 Primary Research
4. Artificial Intelligence in Fashion Market Landscape
4.1 Market Overview
4.2 PEST Analysis - North America
4.3 Ecosystem Analysis
4.4 Expert Opinions
5. Artificial Intelligence in Fashion Market - Key Market Dynamics
5.1 Key Market Drivers
5.1.1 Availability of a huge amount of data originating from different data sources
5.1.2 Increase in adoption of artificial intelligence in fashion industry to enhance operational efficiency and improve customer experiences
5.2 Key Market Restraints
5.2.1 Concerns related to data privacy and security
5.3 Key Market Opportunities
5.3.1 Huge investments in developing NLP enabled solutions are anticipated to flourish the market growth
5.4 Future Trend
5.4.1 Use of AI for predicting fashion trends
5.5 Impact Analysis of Drivers and Restraints
6. Artificial Intelligence in Fashion Market - North America Market Analysis
6.1 Overview
6.2 North America Artificial Intelligence in Fashion Market Forecast and Analysis
7. North America Artificial Intelligence in Fashion Market - By Offerings
7.1 Overview
7.2 North America Artificial Intelligence in Fashion Market Breakdown, by Offerings, 2018 & 2027
7.3 Solutions
7.4 Services
8. North America Artificial Intelligence in Fashion Market - By Deployment
8.1 Overview
8.2 North America Artificial Intelligence in Fashion Market Breakdown, by Deployment, 2018 & 2027
8.3 On-premise
8.4 Cloud
9. North America Artificial intelligence in fashion Market - By Application
9.1 Overview
9.2 North America Artificial intelligence in fashion Market Breakdown, By Application, 2018 & 2027
9.3 Product Recommendation
9.4 Virtual Assistant
9.5 Product Search and Discovery
9.6 Creative Designing and Trend Forecasting
9.7 Customer Relationship Management (CRM)
9.8 Others
10. North America Artificial intelligence in fashion Market Analysis - By End User Industry
10.1 Overview
10.2 North America Artificial intelligence in fashion Market Breakdown, By End User Industry, 2018 & 2027
10.3 Apparel
10.4 Accessories
10.5 Cosmetics
10.6 Others
11. North America Artificial Intelligence in Fashion Market - Country Analysis
11.1 Overview
11.1.1 North America Artificial Intelligence in Fashion Market Breakdown, by Key Countries
11.1.1.1 US Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)
11.1.1.2 Canada Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)
11.1.1.3 Mexico Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)
12. Artificial Intelligence in Fashion Market - Industry Landscape
12.1 Overview
12.2 Market Initiative
12.3 New Development
12.4 Top Five Company Ranking
13. Company Profiles
13.1 Adobe Inc.
13.2 Alphabet Inc. (Google)
13.3 Amazon.com, Inc.
13.4 Catchoom
13.5 Facebook Inc.
13.6 Huawei Technologies Co., Ltd.
13.7 IBM Corporation
13.8 Microsoft Corporation
13.9 Oracle Corporation
13.10 SAP SE
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Insights into the North America Artificial Intelligence in Fashion Market to 2027 - Drivers, Restraints, Opportunities and Trends -...
This Artificial Intelligence Stock Raised Its Dividend on "Black Thursday" – Nasdaq
As many now know, last Thursday was an historic day in the stock market. On March 13, 2020, the S&P 500 plunged 9.5% in a single day, the worst daily drop since "Black Monday" in 1987. The plunge came the day after President Trump delivered an underwhelming speech that included a European travel ban. However, stocks rallied on Friday after news of more government stimulus, emergency measures to boost testing, and the purchasing of oil for the country's strategic reserve. Negotiations for a comprehensive support package for the economy are also ongoing.
However, one tech company was tuning out the noise. Semiconductor equipment maker Applied Materials (NASDAQ: AMAT) decided to announce an increase in its dividend on the exact same day the market went into freefall. Is that a sign of confidence, or foolishness?
Image source: Getty Images.
Applied Materials announced that it would raise its quarterly dividend by a penny, from $0.21 to $0.22, a 4.8% boost. Applied's dividend yield is now 1.86%, but that's with a very modest 27.5% payout ratio. The higher dividend will be paid out on June 11, to shareholders of record as of May 21. CEO Gary Dickerson said: "We are increasing the dividend based on our strong cash flow performance and ongoing commitment to return capital to shareholders. ... We believe the AI-Big Data era will create exciting long-term growth opportunities for Applied Materials."
Semiconductors and semiconductor equipment companies have historically been known to be cyclical parts of the tech industry. However, it appears Applied Materials believes the overarching trends for faster and smarter semiconductors should help the company power through a near-term economic disruption. As chip-makers make smaller and more advanced chips, Applied's machines are a necessary expenditure.
But can the long-term trends buffer the company in a times of a potential global recession?
It should be known that the semiconductor industry was already in a downturn last year in 2019, and was beginning to come out of it in early 2020. For Applied, last quarter's results exceeded the high end of its previous guidance, with revenue up 11% and earnings per share up 21%.On Feb. 12, management also guided for solid sequential growth in Q2 even while lowering its prior numbers by $300 million because of coronavirus as of that date.
On a Feb. 12 conference call with analysts, Dickerson reiterated that optimism:
We believe we can deliver strong double-digit growth in our semiconductor business this year as our unique solutions accelerate our customers' success in the AI-Big Data era... our current assessment is that the overall impact for fiscal 2020 will be minimal. However, with travel and logistics restrictions, we do expect changes in the timing of revenues during the year. We are actively managing the situation in collaboration with our customers and suppliers.
While many businesses across the world have seen severe interruptions, it's unclear if the chip industry will be affected as much as others, despite its reputation for cyclicality. While consumer-related electronics may take a temporary hit to demand, a more stay-at-home economy means the need for faster connections, which could actually increase demand for servers and base stations.
Memory chip research website DrameXchange released a report on March 13, outlining its current projections for the DRAM and NAND flash industries as of March 1, along with an updated "bear case" scenario should the coronavirus crisis escalate into a global recession, which was updated on March 12.
Category
Current 2020 Projections
Bear Case 2020 Projections
Notebook computer shipments
(2.6%)
(9%)
Server shipments
5.1%
3.1%
Smartphone shipments
(3.5%)
(7.5%)
DRAM price growth
30%
20%
NAND flash price growth
15%
(5%)
Data source: DrameXchange.
Notice that the enterprise-facing server industry looks poised to withstand a potential severe downturn much better than consumer-facing notebook or smartphone industry. In addition, DRAM prices are poised to increase in 2020 even in a recession, as prices had already crashed last year and the industry cut back on capacity. NAND flash had an earlier downturn than DRAM, and was already beginning to come out of it, so it has more potential with a decline in pricing.
In addition, the largest global foundry Taiwan Semiconductor (NYSE: TSM), just said on March 11 that its capacity for leading-edge 5nm chip production was already "fully booked," and that volume production would begin in April. That indicates continued strong demand for leading-edge logic chips.
So while there may be some more softness in certain parts of the chip industry, there are still relatively strong segments as well. Therefore, Applied may not face revenue declines in 2020, but rather a mere absence of previously forecast growth. Yet even if that happens, growth will likely be deferred to 2021, not totally lost, as eventually the demand for chips will increase.
After its decline, Applied Materials stock trades at just 17 times trailing earnings, and just 14.7 times projected 2020 earnings, though 2020 projections may come down. Still, that's a reasonable price to pay for Applied, especially in a zero-interest rate environment. The company has just as much cash as debt, and its recent dividend raise on the market's darkest day in recent history shows long-term confidence. Risk-tolerant investors with a long enough time horizon thus may want to give Applied -- and the entire chip sector -- a look after the dust settles.
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Billy Duberstein owns shares of Applied Materials and Taiwan Semiconductor Manufacturing. His clients may own shares of the companies mentioned. The Motley Fool owns shares of and recommends Taiwan Semiconductor Manufacturing. The Motley Fool recommends Applied Materials. The Motley Fool has a disclosure policy.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
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This Artificial Intelligence Stock Raised Its Dividend on "Black Thursday" - Nasdaq
The Evolution of Artificial Intelligence and Future of National Security – The National Interest
Artificial intelligence is all the rage these days. In the popular media, regular cyber systems seem almost passe, as writers focus on AI and conjure up images of everything from real-life Terminator robots to more benign companions. In intelligence circles, Chinas uses of closed-circuit television, facial recognition technology, and other monitoring systems suggest the arrival of Big Brotherif not quite in 1984, then only about forty years later. At the Pentagon, legions of officers and analysts talk about the AI race with China, often with foreboding admonitions that the United States cannot afford to be second in class in this emerging realm of technology. In policy circles, people wonder about the ethics of AIsuch as whether we can really delegate to robots the ability to use lethal force against Americas enemies, however bad they may be. A new report by the Defense Innovation Board lays out broad principles for the future ethics of AI, but only in general terms that leave lots of further work to still be done.
What does it all really mean and is AI likely to be all its cracked up to be? We think the answer is complex and that a modest dose of cold water should be thrown on the subject. In fact, many of the AI systems being envisioned today will take decades to develop. Moreover, AI is often being confused with things it is not. Precision about the concept will be essential if we are to have intelligent discussions about how to research, develop, and regulate AI in the years ahead.
AI systems are basically computers that can learn how to do things through a process of trial and error with some mechanism for telling them when they are right and when they are wrongsuch as picking out missiles in photographs, or people in crowds, as with the Pentagon's "Project Maven"and then applying what they have learned to diagnose future data. In other words, with AI, the software is built by the machine itself, in effect. The broad computational approach for a given problem is determined in advance by real old-fashioned humans, but the actual algorithm is created through a process of trial and error by the computer as it ingests and processes huge amounts of data. The thought process of the machine is really not that sophisticated. It is developing artificial instincts more than intelligenceexamining huge amounts of raw data and figuring out how to recognize a cat in a photo or a missile launcher on a crowded highway rather than engaging in deep thought (at least for the foreseeable future).
This definition allows us quickly to identify some types of computer systems that are not, in fact, AI. They may be important, impressive, and crucial to the warfighter but they are not artificial intelligence because they do not create their own algorithms out of data and multiple iterations. There is no machine learning involved, to put it differently. As our colleague, Tom Stefanick, points out, there is a fundamental difference between advanced algorithms, which have been around for decades (though they are constantly improving, as computers get faster), and artificial intelligence. There is also a difference between an autonomous weapons system and AI-directed robotics.
For example, the computers that guide a cruise missile or a drone are not displaying AI. They follow an elaborate, but predetermined, script, using sensors to take in data and then putting it into computers, which then use software (developed by humans, in advance) to determine the right next move and the right place to detonate any weapons. This is autonomy. It is not AI.
Or, to use an example closer to home for most people, when your smartphone uses an app like Google Maps or Waze to recommend the fastest route between two points, this is not necessarily, AI either. There are only so many possible routes between two places. Yes, there may be dozens or hundredsbut the number is finite. As such, the computer in your phone can essentially look at each reasonable possibility separately, taking in data from the broader network that many other peoples phones contribute to factor traffic conditions into the computation. But the way the math is actually done is straightforward and predetermined.
Why is this important? For one thing, it should make us less breathless about AI, and see it as one element in a broader computer revolution that began in the second half of the twentieth century and picked up steam in this century. Also, it should help us see what may or may not be realistic and desirable to regulate in the realm of future warfare.
The former vice chairman of the joint chiefs of staff, Gen. Paul Selva, has recently argued that the United States could be about a decade away from having the capacity to build an autonomous robot that could decide when to shoot and whom to killthough he also asserted that the United States had no plans actually to build such a creature. But if you think about it differently, in some ways weve already had autonomous killing machines for a generation. That cruise missile we discussed above has been deployed since the 1970s. It has instructions to fly a given route and then detonate its warhead without any human in the loop. And by the 1990s, we knew how to build things like skeet submunitions that could loiter over a battlefield and look for warm objects like tanksusing software to decide when to then destroy them. So the killer machine was in effect already deciding for itself.
Even if General Selva's terminator is not built, robotics will in some cases likely be given greater decisionmaking authority to decide when to use force, since we have in effect already crossed over this threshold. This highly fraught subject requires careful ethical and legal oversight, to be sure, and the associated risks are serious. Yet the speed at which military operations must occur will create incentives not to have a person in the decisionmaking loop in many tactical settings. Whatever the United States may prefer, restrictions on automated uses of violent force would also appear relatively difficult to negotiate (even if desirable), given likely opposition from Russia and perhaps from other nations, as well as huge problems with verification.
For example, small robots that can operate as swarms on land, in the air or in the water may be given certain leeway to decide when to operate their lethal capabilities. By communicating with each other, and processing information about the enemy in real-time, they could concentrate attacks where defenses are weakest in a form of combat that John Allen and Amir Husain call hyperwar because of its speed and intensity. Other types of swarms could attack parked aircraft; even small explosives, precisely detonated, could disable wings or engines or produce secondary and much larger explosions. Many countries will have the capacity to do such things in the coming twenty years. Even if the United States tries to avoid using such swarms for lethal and offensive purposes, it may elect to employ them as defensive shields (perhaps against North Korean artillery attack against Seoul) or as jamming aids to accompany penetrating aircraft. With UAVs that can fly ten hours and one hundred kilometers now costing only in the hundreds of thousands of dollars, and quadcopters with ranges of a kilometer more or less costing in the hundreds of dollars, the trendlines are clearand the affordability of using many drones in an organized way is evident.
Where regulation may be possible, and ethically compelling, is limiting the geographic and temporal space where weapons driven by AI or other complex algorithms can use lethal force. For example, the swarms noted above might only be enabled near a ship, or in the skies near the DMZ in Korea, or within a small distance of a military airfield. It may also be smart to ban letting machines decide when to kill people. It might be tempting to use facial recognition technology on future robots to have them hunt the next bin Laden, Baghdadi, or Soleimani in a huge Mideastern city. But the potential for mistakes, for hacking, and for many other malfunctions may be too great to allow this kind of thing. It probably also makes sense to ban the use of AI to attack the nuclear command and control infrastructure of a major nuclear power. Such attempts could give rise to use them or lose them fears in a future crisis and thereby increase the risks of nuclear war.
We are in the early days of AI. We cant yet begin to foresee where its going and what it may make possible in ten or twenty or thirty years. But we can work harder to understand what it actually isand also think hard about how to put ethical boundaries on its future development and use. The future of warfare, for better or for worse, is literally at stake.
Retired Air Force Gen. Lori Robinson is a nonresident senior fellow on the Security and Strategy team in the Foreign Policy program at Brookings. She was commander of all air forces in the Pacific.
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The Evolution of Artificial Intelligence and Future of National Security - The National Interest
Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used To Fight The Pandemic – Forbes
Since the first report of coronavirus (COVID-19) in Wuhan, China, it has spread to at least 100 other countries. As China initiated its response to the virus, it leaned on its strong technology sector and specifically artificial intelligence (AI), data science, and technology to track and fight the pandemic while tech leaders, including Alibaba, Baidu, Huawei and more accelerated their company's healthcare initiatives. As a result, tech startups are integrally involved with clinicians, academics, and government entities around the world to activate technology as the virus continues to spread to many other countries. Here are 10 ways artificial intelligence, data science, and technology are being used to manage and fight COVID-19.
Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used To Fight The Pandemic
1. AI to identify, track and forecast outbreaks
The better we can track the virus, the better we can fight it. By analyzing news reports, social media platforms, and government documents, AI can learn to detect an outbreak. Tracking infectious disease risks by using AI is exactly the service Canadian startup BlueDot provides. In fact, the BlueDots AI warned of the threat several days before the Centers for Disease Control and Prevention or the World Health Organization issued their public warnings.
2. AI to help diagnose the virus
Artificial intelligence company Infervision launched a coronavirus AI solution that helps front-line healthcare workers detect and monitor the disease efficiently. Imaging departments in healthcare facilities are being taxed with the increased workload created by the virus. This solution improves CT diagnosis speed. Chinese e-commerce giant Alibaba also built an AI-powered diagnosis system they claim is 96% accurate at diagnosing the virus in seconds.
3. Process healthcare claims
Its not only the clinical operations of healthcare systems that are being taxed but also the business and administrative divisions as they deal with the surge of patients. A blockchain platform offered by Ant Financial helps speed up claims processing and reduces the amount of face-to-face interaction between patients and hospital staff.
4. Drones deliver medical supplies
One of the safest and fastest ways to get medical supplies where they need to go during a disease outbreak is with drone delivery. Terra Drone is using its unmanned aerial vehicles to transport medical samples and quarantine material with minimal risk between Xinchang Countys disease control centre and the Peoples Hospital. Drones also are used to patrol public spaces, track non-compliance to quarantine mandates, and for thermal imaging.
5. Robots sterilize, deliver food and supplies and perform other tasks
Robots arent susceptible to the virus, so they are being deployed to complete many tasks such as cleaning and sterilizing and delivering food and medicine to reduce the amount of human-to-human contact. UVD robots from Blue Ocean Robotics use ultraviolet light to autonomously kill bacteria and viruses. In China, Pudu Technology deployed its robots that are typically used in the catering industry to more than 40 hospitals around the country.
6. Develop drugs
Googles DeepMind division used its latest AI algorithms and its computing power to understand the proteins that might make up the virus, and published the findings to help others develop treatments. BenevolentAI uses AI systems to build drugs that can fight the worlds toughest diseases and is now helping support the efforts to treat coronavirus, the first time the company focused its product on infectious diseases. Within weeks of the outbreak, it used its predictive capabilities to propose existing drugs that might be useful.
7. Advanced fabrics offer protection
Companies such as Israeli startup Sonovia hope to arm healthcare systems and others with face masks made from their anti-pathogen, anti-bacterial fabric that relies on metal-oxide nanoparticles.
8. AI to identify non-compliance or infected individuals
While certainly a controversial use of technology and AI, Chinas sophisticated surveillance system used facial recognition technology and temperature detection software from SenseTime to identify people who might have a fever and be more likely to have the virus. Similar technology powers "smart helmets" used by officials in Sichuan province to identify people with fevers. The Chinese government has also developed a monitoring system called Health Code that uses big data to identify and assesses the risk of each individual based on their travel history, how much time they have spent in virus hotspots, and potential exposure to people carrying the virus. Citizens are assigned a color code (red, yellow, or green), which they can access via the popular apps WeChat or Alipay to indicate if they should be quarantined or allowed in public.
9. Chatbots to share information
Tencent operates WeChat, and people can access free online health consultation services through it. Chatbots have also been essential communication tools for service providers in the travel and tourism industry to keep travelers updated on the latest travel procedures and disruptions.
10.Supercomputers working on a coronavirus vaccine
The cloud computing resources and supercomputers of several major tech companies such as Tencent, DiDi, and Huawei are being used by researchers to fast-track the development of a cure or vaccine for the virus. The speed these systems can run calculations and model solutions is much faster than standard computer processing.
In a global pandemic such as COVID-19, technology, artificial intelligence, and data science have become critical to helping societies effectively deal with the outbreak.
For more on AI and technology trends, see Bernard Marrs bookArtificial Intelligence in Practice: How 50 Companies Used AI and Machine Learning To Solve Problemsand his forthcoming bookTech Trends in Practice: The 25 Technologies That Are Driving The 4ThIndustrial Revolution, which is available to pre-order now.
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Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used To Fight The Pandemic - Forbes
Blockchain and Artificial Intelligence Convergence Powering the Robotics Capability – EnterpriseTalk
Enterprises are using multiple applications powered by the convergence of blockchain and artificial intelligence, to increase efficiency and effectiveness of RPA.
It is common knowledge that Robotics is powered by artificial intelligence delivering excellence and efficiency in well-known areas- cryptocurrencies, chatbots, or voice-assisted technologies.
Blockchain In the Times of AI
The field of robotics is immensely challenging, and to grow in this segment, companies need to offer reliable and affordable solutions to their clients and customers.
The exciting news is that RPA is also one of the most promising areas utilizing the convergence of Blockchain and AI. This convergence is now showing never before massive efficiencies in the field of robotics.
Robotics has gained massive popularity across industries over the years using artificial intelligence, making all processes more effective and error-free. Now, blockchain will keep the data decentralized and free from any central or concentrated control. By combining the decentralized power of blockchain with the agility of artificial intelligence, the field of robotics can be elevated and advanced in several ways.
The features offered by artificial intelligence will increase the efficiency of robots using automation multi-fold, while data immutability offered by blockchain will tamper-proof the processes. Leveraging these technologies simultaneously to the robotics, the operating mechanism is pre-set to achieve the desired objectives and business goals.
Swarm Robotics: The one to be benefitted the most?
The significance of artificial intelligence and blockchain is the most prominent in the case of Swarm Robotics. This is mainly because both these innovations can be applied collectively to control a group of robots. AI controls every Swarm Robot as it operates according to the pre-set principles and requirements. The collective response and behavior of the Robots can be significantly enhanced with the application of artificial intelligence and blockchain.
AIoT Convergence of Artificial Intelligence with the Internet of Things
This convergence has enormous benefits on scalability with the enhanced scope of operations. Global enterprises have already started witnessing the application of blockchain and artificial intelligence with the Swarm robotics gaining popularity, specifically in the areas related to entertainment, healthcare, and farming. Although several stakeholders have explicitly expressed concerns about the security and safety of the features, there is hardly any negative view about the potential of applications to benefit the industry. Blockchain is a credible technology measure to alleviate the concerns of the stakeholders about the privacy and secrecy of the data. Using the secure cryptographic signatures and other advanced technologies available in the blockchain space, security, and safety concerns regarding robots can be easily handled.
Artificial intelligence will power the Robots while continuing to be the strength of this integration, while Blockchain technology will be playing a passive role by providing backup support to ensure data security and safety. Hence, with this convergence is applied to robotics in an integrated manner, robotics will transform and benefit the industry in an unbelievably positive way.
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Blockchain and Artificial Intelligence Convergence Powering the Robotics Capability - EnterpriseTalk
Artificial intelligence And Smartphone Photography: How Tech Makes You Look Like A Pro (infographic) – Digital Information World
Have you noticed lately how talented everyone you know is at taking pictures? There used to be skill involved in photography, but thanks to theArtificial intelligence (AI) in your smartphones camera everyone can be Annie Leibovitz. But just how does that technology work to reduce the blur from your shaking hands or fix the lighting when you are taking photos in dark places?Artificial Intelligence Mimics Human SightIn our brains there are all kinds of things happening to ensure we can see clearly. Our brains filter out tiny movements and adjust the processing of the images so that they appear focused and make sense. But when you have a normal camera in your shaky hands those images often come out blurred. Artificial intelligence algorithms within your smartphone camera can adjust for your shaking hands, but thats not all. It can also adjust for poor lighting conditions, including darkness, it can change scene modes according to what youre taking a picture of, and it can detect faces and ensure eyes are open and people are smiling.
Artificial intelligence can also take multiple photos within a few milliseconds and stitch together the best parts of each photo for one really excellent composite photo. High dynamic range combines the best elements of three photos, while Top Shot actually takes a short video and combines the best elements into one photo.
In addition to these features, your smartphone camera can blur the background in a portrait, smooth your wrinkles, hide your blemishes, and more. 42% of Americans choose Portrait Mode, which combines background blur with beautification for the perfect selfie every time.
There are also apps that can further enhance your smartphone photos and videos:
When you choose your next smartphone, how important is the camera inside? Learn more about smartphone cameras and all the apps and add-ons that can turn you into a professional photographer from the infographic below. Are you ready to become the next Ansel Adams?
Read next: A comparison between properties of the most used image file types (infographic)
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Artificial intelligence And Smartphone Photography: How Tech Makes You Look Like A Pro (infographic) - Digital Information World
Trepper: The Anti-Bigotry App That Uses Artificial Intelligence to Identify White Nationalists – Capital Research Center
A nonprofit organization claims to have created a phone app that uses artificial intelligence to identify people the group believes to be known white nationalists. Of course, the organization considers people and organizations to be white nationalists even if theyre just regular conservativesand even if theyre not white.
IREHR Sees White Nationalists Everywhere
In February, the Kansas City Star ran a widely circulated hit piece on America First Students, a new student organization at Kansas State University. The smear campaign was based on research by the Institute for Education and Research on Human Rights (IREHR). Despite being a little-known and little-funded organization, IREHR regularly publishes large volumes of research and has even created a bigotry tracking app that promises to use artificial intelligence (AI) and machine learning to out known white nationalists.
Founded in 1983, IREHRs vision, according to its founder Leonard Zeskind, is to fight against what it sees as white nationalism. Zeskind claims that white nationalism is in a symbiotic relationship with mainstream conservatism, such as the Christian right and paleoconservativism. In 2011, Zeskind wrote about IREHRs Re-Birth in the Huffington Post, explicitly attacking the Tea Party movement for its racism in his second sentence. Tea Party Nationalism even has its own section on the IREHR website, right under Race, Racism & White Nationalism. In 2018, years after the Tea Party movement was at its peak, IREHR ran a piece titled Guns and Racism: From the National Rifle Association to Far Right Militias and the Tea Party. The Tea Party movement is such a target of IREHRs ire thatZeskind, along with IREHR president Devin Burghart, wrote a report called Tea Party Nationalism, commissioned by the NAACP. The NAACP also created a companion website: TeaPartyNationalism.com.
In addition to going after the Tea Party movement and the National Rifle Association, IREHR also targets the American Conservative Unions Political Action Conference (CPAC). In 2014, Burghart published The Unbearable Whiteness of CPAC.
Given how IREHR sees white nationalism throughout mainstream conservatism and considers whiteness as something unbearable, its phone application that promises to identify known white nationalists via artificial intelligence sounds incredibly Orwellian.
The Trepper App
Burghart announced the creation of IREHRs anti-bigotry app during a Holocaust Remembrance Day Speech in May 2019. The app is named after Leopold Trepper, the head of a Soviet anti-Nazi spy ring during WWII known as the Red Orchestra. Trepper was later imprisoned by Stalin, reportedly because Trepperhimself Jewishsurrounded himself with Jews. (This detail was not included in IREHRs speech on Holocaust Remembrance Day; however, IREHRs founder and president still generally appreciates communist figures, even visiting the grave of Marxist philosopher Antonio Gramsci.)
During the speech, Burghart boasted that the app now allows us to use the latest in machine learning and artificial intelligence to see if people in the videos you submit are known white nationalists. Machine learning and artificial intelligence are often codewords for facial recognition software. It is unclear, however, how exactly machine learning and artificial intelligence are being used in the app.
Trepper: The Anti-Bigotry App promises to provide instant updates about new threats near you. According to the IREHR website, the app will allow users to receive push notifications of seemingly white nationalist activity, and it allows users to upload their own events, photos, and videos to the app.
Downloading Trepper, users are greeted with a page telling them what they can find on the app: a news tab, a Tools for Response and Resistance tab, and a way to Report incidents (see the screenshot below).
The Report Bigotry tab allows users to request help (a feature coming soon), record video of an ongoing live event, or write a report about an instance of bigotry they witnessed.
The reporting feature lets users provide details about a reportedly bigotry-related event, ranging from murder, the first item on the list, to something internet-based or other.
Much of the app is still under construction. The signs and symbols of bigotry section of the response toolkit is coming soon as is the help, FAQ, tips, etc. section. The section for users to find or create their own anti-bigotry groups loads a blank page. As of writing, the only video uploaded to the Trepper app is a video of a Patriot Prayer rally from 2018. Patriot Prayer is a Portland-based group that has been maligned by the Southern Poverty Law Center (SPLC) and targeted by Antifa activists.
Curiously, Patriot Prayer is included on the Trepper app even though its controversial founder, Joey Gibson, identifies as Japanese, not Caucasian, and has repeatedly condemned white supremacy. Apparently, to the IREHR app, a person doesnt even need to be white to be a white nationalist.
In fact, in the security section of the response toolkit, the Trepper app deviates from merely attacking white nationalism: It suggests ways to protect against the far right, whomever IREHR deems the far right to be. The security section of the app tells users to protect their offices and homes from possible far right infiltrators:
The app also tells users involved in their anti-racist activity to shred documents, handle potential hate mail with tongs, and circulate pictures online of people they suspect to be following them.
Who Is Funding IREHR?
A section of the IREHR app is devoted to learning more about IREHR, with the option to donate to IREHR. According to tax filings, the organization has not had gross receipts of more than $50,000 since 2008. It is unclear how such a small organization with so little funding can create such a complicated appwith the potential of harnessing facial recognition technology.
Garbage In, Garbage Out
IREHRs plans to implement what appears to be facial recognition technology to identify people accused of having the wrong political beliefs is terrifying. IREHR has a history of identifying people, all on its own, as white nationalists who are not white nationalists at all. IREHR already attacks mainstream conservative groups and mainstream conservative ideas, groups, and figures as white nationalist, or as being tied to white nationalism. It is a terrible precedent in general to use artificial intelligence to identify (and catalog) people because they have the wrong political beliefs.
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Trepper: The Anti-Bigotry App That Uses Artificial Intelligence to Identify White Nationalists - Capital Research Center
Artificial intelligence won’t rule the world so long as humans rule AI – The Age
Four days later, the Vatican issued a paper calling for "new forms of regulation" of AI based on the principles of "transparency, inclusion, responsibility, impartiality, reliability, security and privacy".
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The striking thing about both these pronouncements is the degree to which they align with the official line from Silicon Valley, which couches ethics as a set of voluntary principles that will guide, rather than direct, the development of AI.
By proposing broad principles, which are notoriously difficult to define legally, they avoid the guard rails or red lines that would give genuine oversight over the way this technology develops.
The other problem with these voluntary codes is they will always be in conflict with the key drivers of technological change: to make money (if you are a business) or save money (if you are a government).
But theres an alternative approach to harnessing technological change that warrants serious consideration. It is proposed by the Australian Human Rights Commission. Rather than woolly guiding principles, Commissioner Ed Santow argues that AI should be developed within three clear parameters.
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First, it should comply with human rights law. Second, it should be used in ways that minimise harm. Finally, humans need to be accountable for the way AI is used. The difference with this approach is that it anchors AI development within the existing legal framework.
To legally operate in Australia, under this proposal, the development of artificial intelligence would need to ensure it did not discriminate on the grounds of gender, race or social demographic, either directly or in effect.
The AI proponents would also need to show they had thought through the impact of their technology, much like a property developer needs to conduct an environmental impact statement before building.
And critically, an AI tool should have a human a flesh-and-blood person who is responsible for its design and operation.
How would these principles work in practice? Its worth looking at the failed robodebt program, under which recipients of government benefits were sent letters demanding they repay money because they had been overpaid.
If it had been scrutinised before it went live, robodebt is likely to have been found discriminatory, as it shifted the onus of proof onto people from societys most marginalised groups to show their payments were valid.
If it had been subject to a public impact review, the glaring anomalies and inconsistencies in matching Australian Tax Office and social security information would have become apparent before it was trialled on vulnerable people. And if a human had been accountable for its operation, those who received a notice would have had a course of review, rather than feeling as though they were speaking to a machine.
The whole costly and destructive debacle might have been prevented.
Embracing a future where these "disruptive" technologies remake our society guided by voluntary ethical principles is not good enough. As Robert-Elliott Smith observes in his excellent book Rage Inside the Machine, the idea that AI is amoral is bunkum. The values and priorities of the humans who commission and design it will determine the end product.
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This challenge will become more pressing as algorithms begin to process banks of photos and video that purport to "recognise" individuals, track their movements and predict their motivations. The Human Rights Commission report calls for a moratorium on the use of this technology in high-stakes areas such as policing. It seeks to protect citizens from "bad" applications, but also to provide an incentive for industry to support the development of an enforceable legal framework.
Champions of technology may well argue that government intervention will slow down development and risk Australia being "left behind". But if we succeed in ensuring AI is "fair by design", we might end up with a distinctly Australian technology, which reflects our values, to share with the world.
Peter Lewis is the director of the Centre for Responsible Technology.
Peter Lewis is the executive director of Essential, a progressive research and communications company and the director of the Centre for Responsible Technology.
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Artificial intelligence won't rule the world so long as humans rule AI - The Age
Detailed Insights on the Global Artificial Intelligence in Enterprise Communications and Collaboration Market, 2019 – How AI is Making Inroads into…
Dublin, March 12, 2020 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Enterprise Communications and Collaboration, Global, 2019" report has been added to ResearchAndMarkets.com's offering.
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Underpinned by advancements in AI technologies and underlying AI frameworks; significant breakthroughs in processors and computing platforms; and mechanisms to curate data, there are multiple AI applications available today including dedicated virtual assistants, predictive routing, process automation, voice biometrics, interaction recording, speech analytics, real-time transcription, automated forecasting, meeting assistance, automated video framing and many more. These applications are targeted at enriching customer care, employee productivity, and data-driven decision making.
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The study focuses on applications of AI across the following enterprise communications areas:
Key Issues Addressed
Key Topics Covered:
1. Executive Summary
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3. State of the Market
4. Market Trends - Technology Trends
5. End-user Trends - Decision Maker Perceptions of AI
6. Market Drivers and Restraints
7. Developer Ecosystem and Key Competitor Profiles
8. Conclusion
9. Appendix
Companies Mentioned
For more information about this report visit https://www.researchandmarkets.com/r/sf2q1j
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