Category Archives: Artificial Intelligence
FEATURE: How is artificial intelligence changing these five industries? – Nantwich News
Technology is growing at an exponential rate.
Smart devices are integrated into our everyday activities from your homes heating system to the coffee machine.
Artificial intelligence, also known as AI, has developed so quickly that its hard to keep track.
Many of us encounter AI on a daily basis without even realising it.
Technology has transformed all kinds of industries in recent years from retail to public transport.
AI includes robotics, machine learning, automation, natural language processing and much more.
Lets take a closer look at how AI has impacted these five industries.
Education
AI does not suffer from human bias. It can analyse the profiles of children and produce challenges and solutions for each child.
Of course, a good teacher could do the same thing but it would take much longer.
AI is far more efficient and less likely to make a mistake.
AI plays a big role in the development of children these days and can help us identify learning difficulties.
We can also personalise teaching methods through AI. Everyone learns and tests differently.
With AI, we can adapt the classroom to each student and provide a bespoke learning experience.
Retail
Artificial intelligence can streamline processes and improve customer service.
We have all experienced the frustration of talking to a customer service robot.
In the future, AI will only enhance the customer service experience, and you will still get to talk to real people.
Hopefully, it will help you to access information much more easily and contact customer service reps.
Healthcare
There are likely to be more robots in surgery and virtual nurses.
Sounds terrifying, right? AI will make diagnoses, perform procedures and automate medication services.
Healthcare will become much more efficient, and hopefully, there will be fewer medical negligence cases.
Construction
AI is already embedded in construction power tools.
It can tell you the battery level, temperature and whether anything is broken within the tool.
AI can reduce the number of risks on construction sites and help workers to use tools safely.
But the benefits of machine learning dont stop at safety management.
Director of Product for Milwaukee Power Tools, Steve Matson, commented: There is an interesting runway in terms of what we can do with the machine learning model when applied to locations.
The company has been incorporating new location technology into their tools, making them easier to find. Matson added There is a little bit more secret sauce on the horizon as it pertains to tools.
Public transport
AI analyses the data and best routes available for public transport systems.
You can plan out your journey with the help of artificial intelligence. It will calculate traffic delays, accidents and any roadworks on your journey.
People are far more likely to use public transport when they know exactly where to go and what service to get.
Say goodbye to scanning bus timetables, and hello to the new world of public transport.
Artificial intelligence has greatly benefited the modern world and improved the efficiency of numerous sectors.
Do your research and find out if AI can enhance your life today.
(Pic by mikemacmarketing)
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FEATURE: How is artificial intelligence changing these five industries? - Nantwich News
UMass Amherst and Brigham and Women’s Hospital to Lead New Center on Artificial Intelligence, Aging and Alzheimer’s, Funded by $20 Million NIA Grant -…
UMass Amherst professor Deepak Ganesan
AMHERST, Mass. The University of Massachusetts Amherst and Brigham and Womens Hospital announced today the launch of the new Massachusetts AI and Technology Center for Connected Care in Aging and Alzheimers Disease (MassAITC), which seeks to improve in-home care for older adults and individuals with Alzheimers disease, thanks to a grant from the National Institute on Aging (NIA), which is part of the National Institutes of Health (NIH). The award is expected to total approximately $20 million over five years.
MassAITC is a collaboration between the Commonwealths premier institutions of education and healthincluding UMass Amherst, Brigham and Womens Hospital, Massachusetts General Hospital, Brandeis University and Northeastern University. The center will be housed at UMass Amherst and will leverage extensive expertise, access to patient cohorts and resources of the other partner institutions from around Massachusetts. It is co-led by Deepak Ganesan, professor in UMass Amhersts Robert and Donna Manning College of Information and Computer Sciences (CICS), and Niteesh Choudhry, director of the center for Healthcare Delivery Sciences in the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Womens Hospital and Harvard Medical School.
We are pleased that UMass Amherst will house this new center, which brings together such distinguished institutions from across the Commonwealth, saysUMass Amherst ChancellorKumble R. Subbaswamy.The center will leverage the campuss considerable expertise in AI and life sciences to develop advanced care for Alzheimers patients and address healthcare disparities associated with the disease. Applying groundbreaking research and innovation to real-world problems is central to the mission of the flagship campus.
Artificial intelligence has the potential to transform important areas of science and medicine, but there is a critical need to bring the power of AI to the patients, caregivers and clinicians who need it most, says Paul Anderson, senior vice president of research and education at Brigham and Womens Hospital. This grant will allow experts from across our state to come together to help address this key gap.
More than 90% of older Americans would prefer to stay in their homes as they age. However, the prevalence of chronic illness, including Alzheimer's disease, can make the goal of successful aging at home out of reach without substantial support. While at-home health care technologies hold significant promise, they have not been specifically developed for older adults or Alzheimers patients, caregivers and their clinicians. Further, many current treatment and intervention regimes are limited in terms of their ability to be remotely delivered, managed and adapted to patient needs and caregiver abilities.
MassAITC seeks to address this major healthcare disparity, in part, with advanced AI research and development. Its a difficult problem to develop AI-enhanced sensing technologies that work for people where they are, says Ganesan. How do you get good, useful data? How do you analyze this data and present it to the patient, caregiver and clinician? And then how can you intervene in a timely manner when a problem develops?
The center will bridge these gaps with interdisciplinary research that draws on the perspectives of patients, caregivers, clinicians, behavioral scientists and other stakeholders. These perspectives will then inform the work of teams whose expertise lies in wearable and contactless sensing, artificial intelligence and machine learning.
MassAITC also brings together outstanding capabilities from across the Commonwealth, including state-of-the-art facilities for rapid AI-enhanced technology development and patient cohorts to facilitate validation of these technologies in real-world, at-home settings, says Choudhry.
MassAITC is designed to be a research accelerator, says Benjamin Marlin, MassAITCs associate director and professor in CICS. One of the centers goals is to take work that is still in the lab and transition it toward the field so that it can actually support care and make a measurable change in peoples lives.
Deepak and Ben bring a tremendous amount of expertise and leadership to this new center, and we are immensely proud of the work they are doing to improve patient care and quality of life, says Laura Haas, dean of CICS. MassAITC is a shining example of our colleges numerous activities at the intersection of applied computing and health.
Were working with some of the best health researchers in the world and an exceptional group of advisors, says Ganesan. Not only are we adding UMass Amhersts expertise in AI and wearable devices, but also, thanks to UMass Amhersts Institute for Applied Life Sciences (IALS), we have fantastic core facilities to perform cutting-edge research at the intersection of technology and healthcare.
The MassAITC is funded by NIH grant P30AG073107.
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UMass Amherst and Brigham and Women's Hospital to Lead New Center on Artificial Intelligence, Aging and Alzheimer's, Funded by $20 Million NIA Grant -...
KLA Corporation Opens New Artificial Intelligence-Advanced Computing Lab at Indian Institute of Technology Madras Research Park – PR Newswire India
KLA, a Fortune 500 company with 12,000+ global employees, is a leading supplier of process control and process enabling solutions for the global semiconductor and electronics industry. At KLA India, engineers, data scientists and problem-solvers design solutions that improve the performance of KLA's process control products and facilitate customer success. KLA's new state-of-the-art, high-tech research and development center serves as a cultural and collaboration hub for the engineering teams.
"KLA is at the forefront of using AI technology in our process control systems to identify and isolate critical issues in chip manufacturing," stated Ahmad Khan, president, semiconductor process control at KLA. "To expand the reach of AI in our products and develop the next generation of AI innovations, we created our new AI-ACL research facility. Our researchers and engineers at AI-ACL join the AI experts at our AI Modeling and Center of Excellence in Michigan to form a global team committed to advancing the boundaries of AI, software, image processing and physics modeling."
Officiating over the opening of both facilities, Prof. Bhaskar Ramamurthy, Director of IIT Madras said, "KLA and IIT Madras have been collaborating for over 15 years. We look forward to an expanded collaboration with KLA in AI, advanced parallel computing, and quantum computing research for applications in the semiconductor inspection and metrology domain. The IIT Madras Research Park ecosystem is a perfect enabler for such an industry with academic collaboration that is bringing together our resident experts, top student researchers and industry's best minds. I also congratulate KLA on the grand opening of its new office in RMZ Millenia-II today."
Beyond expanding business in India, KLA prioritizes making a positive impact on the local community. In May, KLA created a $550,000 India pandemic relief fundto aid healthcare facilities in procuring critically-needed equipment in the fight against Covid-19. The donation also supports a long-term investment to expand ICU capacity in regional hospitals and better address the needs of under-privileged communities.
Those interested in careers with KLA India may find more information at http://www.kla.com/careers/locations/India.
About KLA:
KLA develops industry-leading equipment and services that enable innovation throughout the electronics industry. We provide advanced process control and process-enabling solutions for manufacturing wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays. In close collaboration with leading customers across the globe, our expert teams of physicists, engineers, data scientists and problem-solvers design solutions that move the world forward. Additional information may be found atwww.kla.com.
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Israel and Germany Kick Off Digital Cooperation to Boost Artificial Intelligence in Healthcare – Algemeiner
Israel and Germany have launched a joint forum to work on advancing the use of artificial intelligence and machine learning in healthcare, with the two nations keen to learn together from the lessons of the coronavirus pandemic.
As part of a three-year project, the German Israeli Health Forum for Artificial Intelligence will bring together stakeholders from the health ecosystem, startups and experts of both countries to discuss developments, regulations and applications of AI solutions in the health sector. The initiative is funded by the German Federal Ministry of Health and was established together with the European Leadership Network (Elnet).
The forum had its first conference in Berlin at the end of last week, which was attended by Israels Health Minister Nitzan Horowitz and his German counterpart Jens Spahn.
Today it is clearer than ever: countries with a strong public health system are more protected in times of crisis, said Horowitz. The public health system in Israel, which is built on the foundations of social democracy, has saved more lives than anything else in the struggle against corona, and we will continue to strengthen it.
The collaboration offers the opportunity to improve the health system in both countries in the long term, Horowitz remarked.
Germanys healthcare system is the midst of a digital transformation, faced with a growing shortage of skilled healthcare workers and pressing demographic changes.
We can learn a lot from each other, not only in matters of digitization, but also regarding successful vaccination campaigns, said Spahn. We should be guided by Israels example. Israel has shown the world how important booster vaccines shots are. Especially since many of those who didnt get vaccinated until now, wont be convinced anymore.
In recent years Israel has grown into one of the worlds leading nations in the digitalization of health care and the use of AI in fields like medical imaging analysis and data analytics of population groups.
Digital health startups have also developed algorithms to help with the early detection of diseases and to generate more accurate medical diagnoses. Investments in Israeli digital health companies topped $1 billion in the first six months of the year surpassing the annual amounts raised by the sector in 2020 and 2019, according to a report by Start-up Nation Central.
During his stay in Berlin, Horowitz who is one of the few openly gay Knesset members visited a Holocaust memorial to remember the Nazi victims from the LGBT community.
I am proud to be the first foreign minister in the world to visit this important site in Berlin. My hands trembled as I placed this wreath, in the name of the State of Israel, at a monument dedicated to the memory of the victims of the Nazis from the LGBT community, tweeted Horowitz on Sunday.
Tens of thousands were sent to concentration camps, wore pink badges, underwent forced sterilization and horrific experiments. Many thousands were murdered because of who they are, he said.
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Israel and Germany Kick Off Digital Cooperation to Boost Artificial Intelligence in Healthcare - Algemeiner
High-performance, low-cost machine learning infrastructure is accelerating innovation in the cloud – MIT Technology Review
Artificial intelligence and machine learning (AI and ML) are key technologies that help organizations develop new ways to increase sales, reduce costs, streamline business processes, and understand their customers better. AWS helps customers accelerate their AI/ML adoption by delivering powerful compute, high-speed networking, and scalable high-performance storage options on demand for any machine learning project. This lowers the barrier to entry for organizations looking to adopt the cloud to scale their ML applications.
Developers and data scientists are pushing the boundaries of technology and increasingly adopting deep learning, which is a type of machine learning based on neural network algorithms. These deep learning models are larger and more sophisticated resulting in rising costs to run underlying infrastructure to train and deploy these models.
To enable customers to accelerate their AI/ML transformation, AWS is building high-performance and low-cost machine learning chips. AWS Inferentia is the first machine learning chip built from the ground up by AWS for the lowest cost machine learning inference in the cloud. In fact, Amazon EC2 Inf1 instances powered by Inferentia, deliver 2.3x higher performance and up to 70% lower cost for machine learning inference than current generation GPU-based EC2 instances. AWS Trainium is the second machine learning chip by AWS that is purpose-built for training deep learning models and will be available in late 2021.
Customers across industries have deployed their ML applications in production on Inferentia and seen significant performance improvements and cost savings. For example, AirBnBs customer support platform enables intelligent, scalable, and exceptional service experiences to its community of millions of hosts and guests across the globe. It used Inferentia-based EC2 Inf1 instances to deploy natural language processing (NLP) models that supported its chatbots. This led to a 2x improvement in performance out of the box over GPU-based instances.
With these innovations in silicon, AWS is enabling customers to train and execute their deep learning models in production easily with high performance and throughput at significantly lower costs.
Machine learning is an iterative process that requires teams to build, train, and deploy applications quickly, as well as train, retrain, and experiment frequently to increase the prediction accuracy of the models. When deploying trained models into their business applications, organizations need to also scale their applications to serve new users across the globe. They need to be able to serve multiple requests coming in at the same time with near real-time latency to ensure a superior user experience.
Emerging use cases such as object detection, natural language processing (NLP), image classification, conversational AI, and time series data rely on deep learning technology. Deep learning models are exponentially increasing in size and complexity, going from having millions of parameters to billions in a matter of a couple of years.
Training and deploying these complex and sophisticated models translates to significant infrastructure costs. Costs can quickly snowball to become prohibitively large as organizations scale their applications to deliver near real-time experiences to their users and customers.
This is where cloud-based machine learning infrastructure services can help. The cloud provides on-demand access to compute, high-performance networking, and large data storage, seamlessly combined with ML operations and higher level AI services, to enable organizations to get started immediately and scale their AI/ML initiatives.
AWS Inferentia and AWS Trainium aim to democratize machine learning and make it accessible to developers irrespective of experience and organization size. Inferentias design is optimized for high performance, throughput, and low latency, which makes it ideal for deploying ML inference at scale.
EachAWS Inferentiachip contains four NeuronCores that implement a high-performancesystolic arraymatrix multiply engine, which massively speeds up typical deep learning operations, such as convolution and transformers. NeuronCores are also equipped with a large on-chip cache, which helps to cut down on external memory accesses, reducing latency, and increasing throughput.
AWS Neuron, the software development kit for Inferentia, natively supports leading ML frameworks, likeTensorFlow andPyTorch. Developers can continue using the same frameworks and lifecycle developments tools they know and love. For many of their trained models, they can compile and deploy them on Inferentia by changing just a single line of code, with no additional application code changes.
The result is a high-performance inference deployment, that can easily scale while keeping costs under control.
Sprinklr, a software-as-a-service company, has an AI-driven unified customer experience management platform that enables companies to gather and translate real-time customer feedback across multiple channels into actionable insights. This results in proactive issue resolution, enhanced product development, improved content marketing, and better customer service. Sprinklr used Inferentia to deploy its NLP and some of its computer vision models and saw significant performance improvements.
Several Amazon services also deploy their machine learning models on Inferentia.
Amazon Prime Video uses computer vision ML models to analyze video quality of live events to ensure an optimal viewer experience for Prime Video members. It deployed its image classification ML models on EC2 Inf1 instances and saw a 4x improvement in performance and up to a 40% savings in cost as compared to GPU-based instances.
Another example is Amazon Alexas AI and ML-based intelligence, powered by Amazon Web Services, which is available on more than 100 million devices today. Alexas promise to customers is that it is always becoming smarter, more conversational, more proactive, and even more delightful. Delivering on that promise requires continuous improvements in response times and machine learning infrastructure costs. By deploying Alexas text-to-speech ML models on Inf1 instances, it was able to lower inference latency by 25% and cost-per-inference by 30% to enhance service experience for tens of millions of customers who use Alexa each month.
As companies race to future-proof their business by enabling the best digital products and services, no organization can fall behind on deploying sophisticated machine learning models to help innovate their customer experiences. Over the past few years, there has been an enormous increase in the applicability of machine learning for a variety of use cases, from personalization and churn prediction to fraud detection and supply chain forecasting.
Luckily, machine learning infrastructure in the cloud is unleashing new capabilities that were previously not possible, making it far more accessible to non-expert practitioners. Thats why AWS customers are already using Inferentia-powered Amazon EC2 Inf1 instances to provide the intelligence behind their recommendation engines and chatbots and to get actionable insights from customer feedback.
With AWS cloud-based machine learning infrastructure options suitable for various skill levels, its clear that any organization can accelerate innovation and embrace the entire machine learning lifecycle at scale. As machine learning continues to become more pervasive, organizations are now able to fundamentally transform the customer experienceand the way they do businesswith cost-effective, high-performance cloud-based machine learning infrastructure.
Learn more about how AWSs machine learning platform can help your company innovate here.
This content was produced by AWS. It was not written by MIT Technology Reviews editorial staff.
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High-performance, low-cost machine learning infrastructure is accelerating innovation in the cloud - MIT Technology Review
Humans in the loop: it takes people to ensure artificial intelligence success – ZDNet
When it comes to artificial intelligence, don't try to go it alone. IT departments, no matter how skilled and ready developers and data scientists may be, can only go so far past proofs of concept. It takes people --from all corners of the enterprise and working collaboratively -- to deliver AI success,
In discussing lessons learned about AI in recent years, industry experts point to the need to get the people from across the enterprise on board. "A copious amount of training data and elastic compute power are not the cornerstones for successful AI implementations," saysSreedhar Bhagavatheeswaran, global head of Mindtree Consulting.
That cornerstone of AI success is people -- not only AI skills, but involvement from all disciplines, from marketing to supply chain management. In recent years -- and especially over the past year, as the need for automated or unattended processes accelerated, "enterprises learned that they must get stakeholder buy-in, with a true champion for AI within the organization's leadership team," saysDan Simion, VP of AI and analytics at Capgemini Americas.
A concerted AI development and deployment effort also needs "strong governance, internal marketing within the company, and proper training to fuel further adoption of the AI initiatives across the business' functional areas," he adds. The key is being able to showcase the valuable insights being generated by these models,
In efforts to make AI pervasive, "enterprises are now conscious of critical factors such as identifying the right journeys and use cases where AI intervention can make a business impact, operationalizing AI by establishing an AI operations and governance mechanisms, and blending the right proportion of data engineering and AI talent," says Bhagavatheeswaran.
The catch, of course, is many of these efforts get undermined by organizational politics, or simple inertia. AI seems glamorous and promising, but acceptance and adoption takes time. "Companies should plan for the time and effort needed to conduct training sessions, and continuously reinforce the use and benefits of the AI system over the traditional methods," advises Nitin Aggarwal, vice president data analytics at The Smart Cube. "Sharing and celebrating small and frequent wins is a proven catalyst."
AI also needs to have a friendly face, rather than perceptions of robots, software or otherwise, taking the reins of the company. "Make the end user interface business-friendly and intuitive," Aggarwal suggests. "The lower the burden on the end user to understand the insights in terms of 'so what,' the higher the chances of them actually using the system." If possible, he advises having an MLOps team on hand "to ensure the deployed solutions continue to work as expected."
To date, the areas of the business having the most success with AI "are those with direct connections to customer interactions -- such as marketing and sales," says Simion. "These areas are constantly looking to drive revenue, and are more open to innovative new methods and tactics to improve efficiencies, which AI offers." Aggarwal agrees, noting that areas seeing the most initial success with AI include "marketing mix optimization, pricing and promotions ROI improvement, demand forecasting, CRM and hyper-personalization." Lately however, AI's power has also been turned on areas such as supply chain risk management, he adds.
AI is more than technology -- it's new ways of thinking about problems and opportunities. Everyone needs to have access to this powerful new tool, Simion urges. "Make sure everyone across the enterprise is using the same technology stack, so each functional area can have access to the same lessons and insights. Consistency of the technology and the value it can bring is what makes the most difference."
AI adoption also hinges on perceptions that it is fair and accurate, making fighting AI bias is another challenge proponents need to address head-on. Start with the data, Aggarwal states. "As AI algorithms learn from data, make a conscious effort for collecting and feeding richer data, that is corrected for bias and is fairly representative of all classes," he advises.
In most cases, "when you deploy AI models into production at scale, you have automatic tools to monitor the results in real-time," says Simion. "When the AI models are outside of their pre-set boundaries and limits, human intervention is necessary. This is done to ensure AI is performing as expected to drive efficiencies for the business, and it also is done to ensure any issues with AI bias or trust are caught and corrected."
It's critical that humans be kept in the loop, says Aggarwal. "Sometimes human decision making alongside the algorithm is helpful to understand different responses and identify any inherent errors or biases. Human judgement can bring in more awareness, context, understanding and research ability to guide fair decision making. Debiasing should be looked at as an ongoing commitment."
As part of this, companies may benefit by establishing an "AI governance council that reviews not only the business results influenced by their AI initiatives, but is also responsible for explaining the results of specific use cases when needed," says Bhagavatheeswaran.
IT leaders and staff need to receive more training and awareness to alleviate AI bias as well. "It also ties into how staff performance is evaluated and how incentives are aligned," says Aggarwal. "If creating the most accurate AI system is the key result area for a data scientist, chances are that you will get a highly accurate system but one, which may not be the most responsible. Similarly, for all staff, an important training should be on where to look for and how to detect biases in AI, and then reward teams who are able to find and recognize flaws."
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Humans in the loop: it takes people to ensure artificial intelligence success - ZDNet
Vlodomyr Kindratenko Named Director of the Center for Artificial Intelligence Innovation at NCSA – HPCwire
Oct. 29, 2021 NCSA is pleased to announce the appointment of Dr. Vlodomyr Kindratenko as Director of the Center for Artificial Intelligence Innovation(CAII). In this new role, he will be responsible for providing the overall leadership, oversight and management of the center, including developing partnerships and projects at regional and national levels, and overseeing day-to-day operations. Dr. Kindratenko will also be fostering and actively participating in a vigorous research program, with responsibilities for which he is especially adept thanks to his prior experience.
NCSA Engagement Director John Towns says Dr. Kindratenko, a seasoned NCSA researcher with prior NCSA leadership experience, is poised to expand the CAIIs ability to forge new relationships while strengthening existing ones.
Vlad comes into this role transitioning from another NCSA leadership role and as the former lead for the NCSA Innovative Systems Lab, Towns says. He has deep connections with campus with a history of collaboration and teaching. We look forward to expanding and deepening those relationships with a focus on the development of AI methods and applications of them to academic and industry challenges.
In addition to his role at NCSA, Dr. Kindratenko maintains positions as adjunct associate professor in theDepartment of Electrical and Computer Engineeringand a research associate professor in theDepartment of Computer Scienceat theUniversity of Illinois Urbana-Champaign. He currently serves as a department editor of IEEE Computing in Science and Engineering magazine and an associate editor of the International Journal of Reconfigurable Computing. Dr. Kindratenkos work has been funded by the National Science Foundation, NASA, Office of Naval Research, Department of Energy, and industry. He has published over 70 papers in peer-reviewed scientific journals and conference proceedings and holds five U.S. patents. He is a senior member of theInstitute of Electrical and Electronics Engineers(IEEE) andAssociation for Computing Machinery(ACM).
Dr. Kindratenkos research interests include high-performance computing, special-purpose computing architectures, cloud computing and machine learning. His combined interest and experience will contribute to his ability to facilitate collaboration across multiple disciplines and support advancements in AI, leveraging NCSAs cutting-edge technology and expertise.
I am very excited to become the CAII Director, and I am looking forward to growing the Center within NCSA and connecting it with the AI-related activities carried out by the UofI faculty, Kindratenko says. Main goals for the Center include finding ways to bring together the U of I AI research community for a chance to collaborate while aligning academic research with industry challenges and opportunities, and providing students with ways to learn and work in the AI domain. The Center will also partner with leading researchers and technology developers to bring state-of-the-art AI capabilities to the UofI research community.
About NCSA
TheNational Center for Supercomputing Applicationsat theUniversity of Illinois at Urbana-Champaignprovides supercomputing and advanced digital resources for the nations science enterprise. At NCSA, University of Illinois faculty, staff, students and collaborators from around the globe use these resources to address research challenges for the benefit of science and society. NCSA has been advancing many of the worlds industry giants for over 35 years by bringing industry, researchers and students together to solve grand challenges at rapid speed and scale.
Source: NCSA
Expert warns that artificial intelligence could soon be able to ‘hack’ human beings | TheHill – The Hill
A world-renowned historian and philosopher is warning that humanity needs to begin regulating artificial intelligence and data collection globally or risk being hacked.
In an upcoming interview with CBSs 60 Minutes, bestselling author Yuval Harari said the nations and large corporations that control the biggest share of data on consumers will control the world in the future, noting that the raw data is worth much more than money.
The world is increasingly kind of cut up into spheres of data collection, of data harvesting. In the Cold War, you had the Iron Curtain. Now we have the Silicon Curtain, that the world is increasingly divided between the USA and China, Harari told Anderson Cooper.
Harari said the increasing sophistication of AI used in algorithms concentrated in the hands of a powerful few could ultimately result in the manipulation of people.
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Netflix tells us what to watch and Amazon tells us what to buy. Eventually within 10 or 20 or 30 years such algorithms could also tell you what to study at college and where to work and whom to marry and even whom to vote for, Harari told Cooper.
To hack a human being is to get to know that person better than they know themselves. And based on that, to increasingly manipulate you, he said.
Harari emphasized the need for countries to work together to put concrete regulations in place to avoid such a scenario and ensure data and AI arent used to exercise control over the public.
One of his recommendations is to make sure the data isnt consolidated in place, adding Thats a recipe for a dictatorship.
The author noted the rise of AI can also be incredibly beneficial to society, but the question is what is being done with it and who regulates it.
Harari is the author of the 2014 global bestseller Sapiens.
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Expert warns that artificial intelligence could soon be able to 'hack' human beings | TheHill - The Hill
Yeah, were spooked: AI starting to have big real-world impact, says expert – The Guardian
A scientist who wrote a leading textbook on artificial intelligence has said experts are spooked by their own success in the field, comparing the advance of AI to the development of the atom bomb.
Prof Stuart Russell, the founder of the Center for Human-Compatible Artificial Intelligence at the University of California, Berkeley, said most experts believed that machines more intelligent than humans would be developed this century, and he called for international treaties to regulate the development of the technology.
The AI community has not yet adjusted to the fact that we are now starting to have a really big impact in the real world, he told the Guardian. That simply wasnt the case for most of the history of the field we were just in the lab, developing things, trying to get stuff to work, mostly failing to get stuff to work. So the question of real-world impact was just not germane at all. And we have to grow up very quickly to catch up.
Artificial intelligence underpins many aspects of modern life, from search engines to banking, and advances in image recognition and machine translation are among the key developments in recent years.
Russell who in 1995 co-authored the seminal book Artificial Intelligence: A Modern Approach, and who will be giving this years BBC Reith lectures entitled Living with Artificial Intelligence, which begin on Monday says urgent work is needed to make sure humans remain in control as superintelligent AI is developed.
AI has been designed with one particular methodology and sort of general approach. And were not careful enough to use that kind of system in complicated real-world settings, he said.
For example, asking AI to cure cancer as quickly as possible could be dangerous. It would probably find ways of inducing tumours in the whole human population, so that it could run millions of experiments in parallel, using all of us as guinea pigs, said Russell. And thats because thats the solution to the objective we gave it; we just forgot to specify that you cant use humans as guinea pigs and you cant use up the whole GDP of the world to run your experiments and you cant do this and you cant do that.
Russell said there was still a big gap between the AI of today and that depicted in films such as Ex Machina, but a future with machines that are more intelligent than humans was on the cards.
I think numbers range from 10 years for the most optimistic to a few hundred years, said Russell. But almost all AI researchers would say its going to happen in this century.
One concern is that a machine would not need to be more intelligent than humans in all things to pose a serious risk. Its something thats unfolding now, he said. If you look at social media and the algorithms that choose what people read and watch, they have a huge amount of control over our cognitive input.
The upshot, he said, is that the algorithms manipulate the user, brainwashing them so that their behaviour becomes more predictable when it comes to what they chose to engage with, boosting click-based revenue.
Have AI researchers become spooked by their own success? Yeah, I think we are increasingly spooked, Russell said.
It reminds me a little bit of what happened in physics where the physicists knew that atomic energy existed, they could measure the masses of different atoms, and they could figure out how much energy could be released if you could do the conversion between different types of atoms, he said, noting that the experts always stressed the idea was theoretical. And then it happened and they werent ready for it.
The use of AI in military applications such as small anti-personnel weapons is of particular concern, he said. Those are the ones that are very easily scalable, meaning you could put a million of them in a single truck and you could open the back and off they go and wipe out a whole city, said Russell.
Russell believes the future for AI lies in developing machines that know the true objective is uncertain, as are our preferences, meaning they must check in with humans rather like a butler on any decision. But the idea is complex, not least because different people have different and sometimes conflicting preferences, and those preferences are not fixed.
Russell called for measures including a code of conduct for researchers, legislation and treaties to ensure the safety of AI systems in use, and training of researchers to ensure AI is not susceptible to problems such as racial bias. He said EU legislation that would ban impersonation of humans by machines should be adopted around the world.
Russell said he hoped the Reith lectures would emphasise that there is a choice about what the future holds. Its really important for the public to be involved in those choices, because its the public who will benefit or not, he said.
But there was another message, too. Progress in AI is something that will take a while to happen, but it doesnt make it science fiction, he said.
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Yeah, were spooked: AI starting to have big real-world impact, says expert - The Guardian
Artificial Intelligence in Healthcare Market worth $67.4 billion by 2027 – Exclusive Report by MarketsandMarkets – Yahoo Finance
CHICAGO, Oct. 29, 2021 /PRNewswire/ -- According to the new market research report "Artificial Intelligence in Healthcare Market by Offering (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-aware Computing, Computer Vision), Application, End User and Geography - Global Forecast to 2027", published by MarketsandMarkets, the market is projected to grow from USD 6.9 billion in 2021 to USD 67.4 billion by 2027; it is expected to grow at a CAGR of 46.2%from 2021 to 2027.
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The key factors fueling the growth of the market include market influx of large and complex healthcare datasets, growing need to reduce healthcare costs, improving computing power and declining hardware cost, rising number of partnerships and collaborations among different domains in healthcare sector, and surging need for improvised healthcare services due to imbalance between health workforce and patients. Additionally, growing potential of AI-based tools for elderly care, increasing focus on developing human-aware AI systems, and rising potential of AI technology in genomics, drug discovery, and imaging & diagnostics to fight COVID-19 is expected to create a growth opportunity for the artificial intelligence in healthcare market.
The software segment is projected to account for the largest share of the artificial intelligence in healthcare market during the forecast period.
Many companies are developing software solutions for various healthcare applications; this is the key factor complementing the growth of the software segment. Strong demand among software developers (especially in medical centers and universities) and widening applications of AI in the healthcare sector are among the prime factors complementing the growth of the AI platform within the software segment. Google AI Platform, TensorFlow, Microsoft Azure, Premonition, Watson Studio, Lumiata, and Infrrd are some of the top AI platforms.
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The market for machine learning segment is expected to grow at the highest CAGR during the forecast period
The increasing adoption of machine learning technology (especially deep learning) in various healthcare applications such as inpatient monitoring & hospital management, drug discovery, medical imaging & diagnostics, and cybersecurity is driving the adoption of machine learning technology in the AI in healthcare market.
The medical imaging & diagnostics segment is expected to grow at the highest CAGR of the artificial intelligence in healthcare market during the forecast period.
The high growth of the medical imaging and diagnostics segment can be attributed to factors such as the presence of a large volume of imaging data, advantages offered by AI systems to radiologists in diagnosis and treatment management, and the influx of a large number of startups in this segment.
North America region is expected to hold the largest share of artificial intelligence in healthcare market during the forecast period.
Increasing adoption of AI technology across the continuum of care, especially in the US, and high healthcare spending combined with the onset of COVID-19 pandemic accelerating the adoption of AI in hospital and clinics across the region are the major factors driving the growth of the North American market.
The key players operating in the artificial intelligence in healthcare market include Intel (US), Koninklijke Philips (Netherlands), Microsoft (US), IBM (US), and Siemens Healthineers (Germany).
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