AI Simplified - A brilliant report

April 15, 2018

Yesterday April 14th was a really wonderful day for me. I had the opportunity of meeting some of the best minds in AI and computer architecture in India like GS Madhusudan, Dr.Shivaram Kalyanakrishnan at a forum on AI. There were a lot of factors that made the interaction really amazing but what impressed me most was the uncomplicated and simple approach that these people take to AI. There were handful of such people at the forum and it would be a really long list to draw out. In short, a full day well spent and learnt quite a bit. Here are a few extracts from a report on AI. I don’t think AI can be explained with greater simplicity than this. People who like the summary can go on and read the report a link to which is shared at the end of the article.

 

What is AI?

 

Artificial Intelligence (AI) is a science and a set of computational technologies that are inspired by—but typically operate quite differently from—the ways people use their nervous systems and bodies to sense, learn, reason, and take action. Twenty-first century AI enables a constellation of mainstream technologies that are having a substantial impact on everyday lives. Computer vision and AI planning, for example, drive the video games that are now a bigger entertainment industry than Hollywood. Deep learning, a form of machine learning based on layered representations of variables referred to as neural networks, has made speech-understanding practical on our phones and in our kitchens, and its algorithms can be applied widely to an array of applications that rely on pattern recognition. Natural Language Processing (NLP) and knowledge representation and reasoning have enabled a machine to beat the Jeopardy champion and are bringing new power to Web searches.

 

Innovations relying on computer-based vision, speech recognition, and Natural Language Processing have driven changes, as have concurrent scientific and technological advances in related fields. AI is also changing how people interact with technology. Many people have already grown accustomed to touching and talking to their smart phones. People’s future relationships with machines will become ever more nuanced, fluid, and personalized as AI systems learn to adapt to individual personalities and goals. These AI applications will help monitor people’s well-being, alert them to risks ahead, and deliver services when needed or wanted.

 

Though drawing from a common source of research, AI technologies have influenced and will continue to influence these domains differently. Each domain faces varied AI-related challenges, including the difficulty of creating safe and reliable hardware for sensing and effecting (transportation and service robots), the difficulty of smoothly interacting with human experts (healthcare and education), the challenge of gaining public trust (low-resource communities and public safety and security), the challenge of overcoming fears of marginalizing humans (employment and workplace) and the risk of diminishing interpersonal interaction (entertainment). Some domains are primarily business sectors, such as transportation and healthcare, while others are more oriented to consumers, such as entertainment and home service robots. Some cut across sectors, such as employment/workplace and low-resource communities.

 

Hot areas of AI research

 

Large-scale machine learning concerns the design of learning algorithms, as well as scaling existing algorithms, to work with extremely large data sets.

 

Deep learning, a class of learning procedures, has facilitated object recognition in images, video labeling, and activity recognition, and is making significant inroads into other areas of perception, such as audio, speech, and natural language processing.

 

Reinforcement learning is a framework that shifts the focus of machine learning from pattern recognition to experience-driven sequential decision-making. It promises to carry AI applications forward toward taking actions in the real world. While largely confined to academia over the past several decades, it is now seeing some practical, real-world successes.

 

Robotics is currently concerned with how to train a robot to interact with the world around it in generalizable and predictable ways, how to facilitate manipulation of objects in interactive environments, and how to interact with people. Advances in robotics will rely on commensurate advances to improve the reliability and generality of computer vision and other forms of machine perception.

 

Computer vision is currently the most prominent form of machine perception. It has been the sub-area of AI most transformed by the rise of deep learning. For the first time, computers are able to perform some vision tasks better than people. Much current research is focused on automatic image and video captioning.

 

Natural Language Processing, often coupled with automatic speech recognition, is quickly becoming a commodity for widely spoken languages with large data sets. Research is now shifting to develop refined and capable systems that are able to interact with people through dialog, not just react to stylized requests. Great strides have also been made in machine translation among different languages, with more real-time person-to-person exchanges on the near horizon.

 

Collaborative systems research investigates models and algorithms to help develop autonomous systems that can work collaboratively with other systems and with humans.

 

Crowdsourcing and human computation research investigates methods to augment computer systems by making automated calls to human expertise to solve problems that computers alone cannot solve well.

 

Algorithmic game theory and computational social choice draw attention to the economic and social computing dimensions of AI, such as how systems can handle potentially misaligned incentives, including self-interested human participants or firms and the automated AI-based agents representing them.

 

Internet of Things (IoT) research is devoted to the idea that a wide array of devices, including appliances, vehicles, buildings, and cameras, can be interconnected to collect and share their abundant sensory information to use for intelligent purposes.

 

Neuromorphic computing is a set of technologies that seek to mimic biological neural networks to improve the hardware efficiency and robustness of computing systems, often replacing an older emphasis on separate modules for input/output, instruction-processing, and memory.

 

The field of AI is shifting toward building intelligent systems that can collaborate effectively with people, including creative ways to develop interactive and scalable ways for people to teach robots.  I have started reading the report and will write simplified articles that anyone can understand with the effect and implications of AI in our day to day lives and also in domains like healthcare, education, learning technologies, law among a lot of others.

As promised attached herewith is a link to the report (https://ai100.stanford.edu/sites/default/files/ai_100_report_0831fnl.pdf), I was just lucky to meet these scholars

 

and listen to them. AI is becoming an important facet of our lives and will definitely have a lot of impact on future generations. Happy reading….

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