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Getting AI to Learn Like a Baby is Goal of Self-Supervised Learning 

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Scientists are studying how to create AI systems that learn from self-supervision, akin to how babies learn from observing their environment. (Credit: Getty Images) 

By AI Trends Staff  

Scientists are working on creating better AI that learns through self-supervision, with the pinnacle being AI that could learn like a baby, based on observation of its environment and interaction with people.  

This would be an important advance because AI has limitations based on the volume of data required to train machine learning algorithms, and the brittleness of the algorithms when it comes to adjusting to changing circumstances. 

Yann LeCun, chief AI scientist at Facebook

“This is the single most important problem to solve in AI today,” stated Yann LeCun, chief AI scientist at Facebook, in an account in the Wall Street Journal. Some early success with self-supervised learning has been seen in the natural language processing used in mobile phones, smart speakers, and customer service bots.   

Training AI today is time-consuming and expensive. The promise of self-supervised learning is for AI to train itself without the need for external labels attached to the data. Dr. LeCun is now focused on applying self-supervised learning to computer vision, a more complex problem in which computers interpret images such as a person’s face.  

The next phase, which he thinks is possible in the next decade or two, is to create a machine that can “learn how the world works by watching video, listening to audio, and reading text,” he stated. 

More than one approach is being tried to help AI learn by itself. One is the neuro-symbolic approach, which combines deep learning and symbolic AI, which represents human knowledge explicitly as facts and rules. IBM is experimenting with this approach in its development of a bot that works alongside human engineers, reading computer logs to look for system failure, understand why a system crashed and offer a remedy. This could increase the pace of scientific discovery, with its ability to spot patterns not otherwise evident, according to Dario Gil, director of IBM Research. “It would help us address huge problems, such as climate change and developing vaccines,” he stated. 

Child Psychologists Working with Computer Scientists on MESS  

DARPA is working with the University of California at Berkeley on a research project, Machine Common Sense, funding collaborations between child psychologists and computer scientists. The system is called MESS, for Model-Building, Exploratory, Social Learning System.   

Alison Gopnik, Professor of Psychology, University of California, Berkeley and the author of “The Philosophical Baby”

“Human babies are the best learners in the universe. How do they do it? And could we get an AI to do the same?,” queried Alison Gopnik, a professor of psychology at Berkeley and the author of “The Philosophical Baby” and “The Scientist in the Crib,” among other books, in a recent article she wrote for the Wall Street Journal.  

“Even with a lot of supervised data, AIs can’t make the same kinds of generalizations that human children can,” Gopnik said. “Their knowledge is much narrower and more limited, and they are easily fooled. Current AIs are like children with super-helicopter-tiger moms—programs that hover over the learner dictating whether it is right or wrong at every step. The helicoptered AI children can be very good at learning to do specific things well, but they fall apart when it comes to resilience and creativity. A small change in the learning problem means that they have to start all over again.” 

The scientists are also experimenting with AI that is motivated by curiosity, which leads to a more resilient learning style, called “active learning” and is a frontier in AI research.  

The challenge of the DARPA Machine Common Sense program is to design an AI that understands the basic features of the world as well as an 18-month-old. “Some computer scientists are trying to build common sense models into the AIs, though this isn’t easy. But it is even harder to design an AI that can actually learn those models the way that children do,” Dr. Gopnik wrote. “Hybrid systems that combine models with machine learning are one of the most exciting developments at the cutting edge of current AI.” 

Training AI models on labeled datasets is likely to play a diminished role as self-supervised learning comes into wider use, LeCun said during a session at the virtual International Conference on Learning Representation (ICLR) 2020, which also included Turing Award winner and Canadian computer scientist Yoshua Bengio.  

The way that self-supervised learning algorithms generate labels from data by exposing relationships between the data’s parts is an advantage.   

“Most of what we learn as humans and most of what animals learn is in a self-supervised mode, not a reinforcement mode. It’s basically observing the world and interacting with it a little bit, mostly by observation in a test-independent way,” stated LeCun, in an account from VentureBeat “This is the type of [learning] that we don’t know how to reproduce with machines.” 

Bengio was optimistic about the potential for AI to gain from the field of neuroscience, in particular for its explorations of consciousness and conscious processing. Bengio predicted that new studies will clarify the way high-level semantic variables connect with how the brain processes information, including visual information. These variables that humans communicate using language could lead to an entirely new generation of deep learning models, he suggested. 

“There’s a lot of progress that could be achieved by bringing together things like grounded language learning, where we’re jointly trying to understand a model of the world and how high-level concepts are related to each other,” said Bengio“Human conscious processing is exploiting assumptions about how the world might change, which can be conveniently implemented as a high-level representation.”  

Bengio Delivered NeurIPS 2019 Talk on System 2 Self-Supervised Models 

At the 2019 Conference on Neural Information Processing Systems (NeurIPS 2019), Bengio spoke on this topic in a keynote speech entitled,  “From System 1 Deep Learning to System 2 Deep Learning,” with System 2 referring to self-supervised models.  

“We want to have machines that understand the world, that build good world models, that understand cause and effect, and can act in the world to acquire knowledge,” he said in an account in TechTalks.  

The intelligent systems should be able to generalize to different distributions in data, just as children learn to adapt as the environment changes around them. “We need systems that can handle those changes and do continual learning, lifelong learning, and so on,” Bengio stated. “This is a long-standing goal for machine learning, but we haven’t yet built a solution to this.”  

Read the source articles in the Wall Street Journal, Alison for the Wall Street Journal, in VentureBeat and in TechTalks. 

Source: https://www.aitrends.com/ai-research/getting-ai-to-learn-like-a-baby-is-goal-of-self-supervised-learning/

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How 5G Will Impact Customer Experience?

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5G is the breakthrough technology promised to bring new innovations, change the way people are traversing through the Internet with its faster connection speeds, lower latency, high bandwidth, and ability to connect one million devices per square kilometre. Telcos are deploying 5G to enhance our day-to-day lives.

“When clubbed with other technologies like Artificial Intelligence, Internet of Things (IoT), it could mean a lot to a proliferation of other technologies like AR/VR, data analytics.” 

5G can be a boon for businesses with the delivery of increased reliability, efficiency and performance if it can be used to drive more value to the customers as well as the business stakeholders and meet their expectations with the help of digital technologies as mentioned below:

Consumer Expectations are on the Rise

In modern days, customer service teams provide and manage customer support via call centres and digital platforms. The rollout of 5G is expected to unleash more benefits with a positive impact on customer service as they improve their present personalized service offerings to customers and allow it to further create new solutions that could develop their customer engagement to win great deals.

For instance, salespeople in a retail store are being imbibed with layers of information about customers’ behaviour and choices that will help them build a rich and tailored experience for the customers walking down the store.

Video Conferencing/streaming is Just a Few Clicks Away

Video support is considered to be a critical part of Consumer Experience (CX) and will open new avenues for consumer-led enterprises.

“As per a survey conducted by Oracle with 5k people, 75% of people understand the efficiency and value of video chat and voice calls.” 

CX representatives used the video support feature to troubleshoot highly technical situations through video chat and screen sharing options with few clicks, potentially reducing the number of in-house technician visits during critical situations like coronavirus pandemic.

Also, nowadays video conferencing is facilitated with an option to record a quick instant video describing the process/solution and discarding the long process of sending step-by-step emails. Enterprises can develop advanced user guide for troubleshooting issues featuring video teasers for resolving common problems.

However, high-definition video quality is preferable for video conferencing, chat and demands for an uninterrupted network with smooth video streaming. This means operators need to carry out network maintenance activities on regular intervals to check whether there is any kind of 5G PIM formation on these network cell towers that could reduce receive sensitivity and performance, thereby deteriorating network speed, video resolution etc.

Thus, PIM testing becomes critical for delivering enhanced network services without interference, necessary for high-resolution online video conferencing, chats, and many more.

Increased Smart Devices and the Ability to Troubleshoot via Self-Service

The inception of 5G will give a boost to the IoT and smart device market which is already growing.

These smart devices IoT connections are expected to become twice in number between 2019 and 2025 i.e. more than 25Bn as per the GSM association which is an industry organization representing telecom operators across the globe.

With lower latency and improvisation in reliability, 5G has a lot more to offer as it connects a large number of devices. This will ultimately curb the manpower needed for customer support thereby reducing labour costs for the enterprise. Moreover, these IoT connected devices and high-speed network of 5G permit consumers to self-troubleshoot these devices at their own homes.

In order to facilitate these high-resolution networks, telecom operators need to perform 5G network testing and identify issues, take corrective actions that could improve their network and integrate with advanced capabilities, making it more efficient than previous connections with the wider network coverage.

Enhanced Augmented Reality (AR) / Virtual Reality (VR) Capabilities

As these tools are being widely used, customers are provided with virtual stores or immersive experiences using AR to view a sneak peek of the products in their house in real-time.

“‘Augmented Retail: The New Consumer Reality’ study by Nielsen in 2019 suggested that AR/VR has created a lot of interest in people and they are willing to use these technologies to check out products.” 

Analysis of Bulk Data With Big Data Analytics

Enterprises have to deal with a huge volume of data daily. 5G has the ability to collect these data and with its advanced network connectivity across a large number of devices, it delivers faster data analytics too.

Companies will be able to process this vast amount of unstructured data sets combined with Artificial Intelligence (AI) to extract meaningful insights and use them for drafting business strategies like using customer behaviour data sets to study their buying behaviour and targeting such segment with customized service offerings as per their requirement.

As per Ericsson’s AI in networks report, 68% of Communications Service Providers (CSPs) believe improving CX is a business objective while more than half of them already believe AI will be a key technology that will assist in improving the overall CX. Thus, big data analytics will be crucial for harnessing all new data and enhance the customer experience.

Conclusion

Looking from a CX point of view, 5G benefits will far extend beyond the experience of a citizen. Real-time decisions will accelerate with the prevalence of 5G and application of other new-age technologies like AI, ML, IoT, etc. As 5G deployment will continue to grow, so is the transition of each trending processes mentioned above that will ultimately improve your business in terms of productivity, gain a large customer base and bring more revenues.

Source: https://www.aiiottalk.com/technology/5g-impact-on-customer-experience/

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Resiliency And Security: Future-Proofing Our AI Future

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Deploying AI in the enterprise means thinking forward for resiliency and security (GETTY IMAGES)

By Allison Proffitt, AI Trends

On the first day of the Second Annual AI World Government conference and expo held virtually October 28-30, a panel moderated by Robert Gourley, cofounder & CTO of OODA, raised the issue of AI resiliency. Future-proofing AI solutions requires keeping your eyes open to upcoming likely legal and regulatory roadblocks, said Antigone Peyton, General Counsel & Innovation Strategist at Cloudigy Law. She takes a “use as little as possible” approach to data, raising questions such as: How long do you really need to keep training data? Can you abstract training data to the population level, removing some risk while still keeping enough data to find dangerous biases?

Stephen Dennis, Director of Advanced Computing Technology Centers at the U.S. Department of Homeland Security, also recommended a forward-looking posture, but in terms of the AI workforce. In particular, Dennis challenged the audience to consider the maturity level of the users of new AI technology. Full automation is not likely a first AI step, he said. Instead, he recommends automating slowly, bringing the team along. Take them a technology that works in the context they are used to, he said. They shouldn’t need a lot of training. Mature your team with the technology. Remove the human from the loop slowly.

Of course, some things will never be fully automated. Brian Drake, U.S. Department of Defense, pointed out that some tasks are inherently human-to-human interactions—such as gathering human intelligence. But AI can help humans do even those tasks better, he said.

He also cautioned enterprises to consider their contingency plan as they automate certain tasks. For example, we rarely remember phone numbers anymore. We’ve outsourced that data to our phones while accepting a certain level of risk. If you deploy a tool that replaces a human analytic activity, that’s fine, Drake said. But be prepared with a contingency plan, a solution for failure.   

Organizing for Resiliency

All of these changes will certainly require some organizational rethinking, the panel agreed. While government is organized in a top down fashion, Dennis said, the most AI-forward companies—Uber, Netflix—organize around the data. That makes more sense, he proposed, if we are carefully using the data.

Data models—like the new car trope—begin degrading the first day they are used. Perhaps the source data becomes outdated. Maybe an edge use case was not fully considered. The deployment of the model itself may prompt a completely unanticipated behavior. We must capture and institutionalize those assessments, Dennis said. He proposed an AI quality control team—different from the team building and deploying algorithms—to understand degradation and evaluate the health of models in an ongoing way. His group is working on this with sister organizations in cyber security, and he hopes the best practices they develop can be shared to the rest of the department and across the government.

Peyton called for education—and reeducation—across organizations. She called the AI systems we use today a “living and breathing animal”. This is not, she emphasized, an enterprise-level system that you buy once and drop into the organization. AI systems require maintenance, and someone must be assigned to that caretaking.

But at least at the Department of Defense, Drake pointed out, all employees are not expected to become data scientists. We’re a knowledge organization, he said, but even if reskilling and retraining are offered, a federal workforce does not have to universally accept those opportunities. However, surveys across DoD have revealed an “appetite to learn and change”, Drake said. The Department is hoping to feed that curiosity with a three-tiered training program offering executive-level overviews, practitioner-level training on the tools currently in place, and formal data science training. He encouraged a similar structure to AI and data science training across other organizations.

Bad AI Actors

Gourley turned the conversation to bad actors. The very first telegraph message between Washington DC and Baltimore in 1844 was an historic achievement. The second and third messages—Gourley said—were spam and fraud. Cybercrime is not new and it is absolutely guaranteed in AI. What is the way forward, Gourley asked the panel.

“Our adversaries have been quite clear about their ambitions in this space,” Drake said. “The Chinese have published a national artificial intelligence strategy; the Russians have done the same thing. They are resourcing those plans and executing them.”

In response, Drake argued for the vital importance of ethics frameworks and for the United States to embrace and use these technologies in an “ethically up front and moral way.” He predicted a formal codification around AI ethics standards in the next couple of years similar to international nuclear weapons agreements now.

Source: https://www.aitrends.com/ai-world-government/deploying-ai-in-the-enterprise-means-thinking-forward-for-resiliency-and-security/

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AI Projects Progressing Across Federal Government Agencies

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The AI World Government Conference kicked off virtually on Oct. 28 and continues on Oct. 29 and 30. Tune in to learn about AI strategies and plans of federal agencies. (Credit: Getty Images)

By AI Trends Staff

Government agencies are gaining experience with AI on projects, with practitioners focusing on defining the project benefit and the data quality is good enough to ensure success. That was a takeaway from talks on the opening day of the Second Annual AI World Government conference and expo held virtually on October 28.

Wendy Martinez, PhD, director of the Mathematical Statistics Research Center, US Bureau of Labor Statistics

Wendy Martinez, PhD, director of the Mathematical Statistics Research Center, with the Office of Survey Methods Research in the US Bureau of Labor Statistics, described a project to use natural language understanding AI to parse text fields of databases, and automatically correlate them to job occupations in the federal system. One lesson learned was despite interest in sharing experience with other agencies, “You can’t build a model based on a certain dataset and use the model somewhere else,”  she stated. Instead, each project needs its own source of data and model tuned to it.

Renata Miskell, Chief Data Officer in the Office of the Inspector General for the US Department of Health and Human Services, fights fraud and abuse for an agency that oversees over $1 trillion in annual spending, including on Medicare and Medicaid. She emphasized the importance of ensuring that data is not biased and that models generate ethical recommendations. For example, to track fraud in its grant programs awarding over $700 billion annually, “It’s important to understand the data source and context,” she stated. The unit studied five years of data from “single audits” of individual grant recipients, which included a lot of unstructured text data. The goal was to pass relevant info to the audit team. “It took a lot of training, she stated. “Initially we had many false positives.” The team tuned for data quality and ethical use, steering away from blind assumptions. “If we took for granted that the grant recipients were high risk, we would be unfairly targeting certain populations,” Miskell stated.

Dave Cook, senior director of AI/ML Engineering Services, Figure Eight Federal

In the big picture, many government agencies are engaged in AI projects and a lot of collaboration is going on. Dave Cook is senior director of AI/ML Engineering Services for Figure Eight Federal, which works on AI projects for federal clients. He has years of experience working in private industry and government agencies, mostly now the Department of Defense and intelligence agencies. “In AI in the government right now, groups are talking to one another and trying to identify best practices around whether to pilot, prototype, or scale up,” he said. “The government has made some leaps over the past few years, and a lot of sorting out is still going on.”

Ritu Jyoti, Program VP, AI Research and Global AI Research lead for IDC consultants, program contributor to the event, has over 20 years of experience working with companies including EMC, IBM Global Services, and PwC Consulting. “AI has progressed rapidly,” she said. From a global survey IDC conducted in March, business drivers for AI adoption were found to be better customer experience, improved employee productivity, accelerated innovation and improved risk management. A fair number of AI projects failed. The main reasons were unrealistic expectations, the AI did not perform as expected, the project did not have access to the needed data, and the team lacked the necessary skills. “The results indicate a lack of strategy,” Joti stated.

David Bray, PhD, Inaugural Director of the nonprofit Atlantic Council GeoTech Center, and a contributor to the event program, posted questions on how data governance challenges the future of AI. He asked what questions practitioners and policymakers around AI should be asking, and how the public can participate more in deciding what can be done with data. “You choose not to be a data nerd at your own peril,” he said.

Anthony Scriffignano, PhD, senior VP & Chief Data Scientist with Dun & Bradstreet, said in the pandemic era with many segments of the economy shut down, companies are thinking through and practicing different ways of doing things. “We sit at the point of inflection. We have enough data and computer power to use the AI techniques invented generations ago in some cases,” he said. This opportunity poses challenges related to what to try and what not to try, and “sometimes our actions in one area cause a disruption in another area.”

AI World Government continues tomorrow and Friday.

(Ed. Note: Dr. Eric Schmidt, former CEO of Google is now chair of the National Security Commission on AI, today was involved in a discussion, Transatlantic Cooperation Around the Future of AI, with Ambassador Mircea Geoana, Deputy Secretary General, North Atlantic Treaty Organization, and Secretary Robert O. Work, vice chair of the National Security Commission. Convened by the Atlantic Council, the event can be viewed here.)

Source: https://www.aitrends.com/ai-world-government/ai-projects-progressing-across-federal-government-agencies/

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