Taking artificial intelligence a step further, a new Socially-Aware Robot Assistant (SARA) took the participants of the World Economic Forum (WEF) here by surprise by understanding their spoken words and non-verbal behaviour to build a relationship. Developed by students and professors of prestigious Carnegie Mellon University (CMU) as a research project, SARA became a key exhibit at the WEF annual meeting held in this ski resort town last week. CMU President Subra Suresh, who was here for the WEF, said the university would also take SARA to other parts of the world, which may include India, for exhibition and eventually, it may be licensed to some company or organisation for commercial use, but any decision on that will be taken by the persons concerned and professors at the university. "With technology advancing so fast, one of the opportunities for us is whether we can combine robotics, artificial intelligence, machine learning etc to create tools that will have positive impact on society. This is how SARA has come out," Suresh told PTI in an interview here on the sidelines of the WEF meet.

Artificial Intelligence proved to be a very unstable field, its 60 year history is a chain of periods filled with excitement and anticipation of the singularity, taking turns with times of total ignorance known as AI winters, when donors lose hope in replacing people by machines, when the general public is satiated with chat- and chess- and fridge-bots. But this time* everything is different. The decade started with a lavish AI spring, flourished with Big Data and the Internet of Things, doped the world with an almost forgotten scent of smart homes and smart cities. This set the stage for something bigger, a new AI summer, a hot and deep summer of Neural Networks: NN based AI, or ANN, also referred today as "real" and "strong" AI: manufactured systems are imitating a network of brain neurons, algorithms are rewriting themselves, computers learning from their mistakes, and showing off with the first successes in science, business, security ... and art. In July 2015 Google released their Deep Dream, a restless algorithm that learned to stare at an image until it sees the dog inside.

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do. "The model performs in the 75th percentile for American adults, making it better than average," said Northwestern Engineering's Ken Forbus. "The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition." The new computational model is built on CogSketch, an artificial intelligence platform previously developed in Forbus' laboratory.

Artificial intelligence (AI) got a lot of press in 2016, not least because of the victory of Google's AI program over Lee Sedol, the world's best Go player. That triumph of machine over human elicited numerous responses, some enthusiastic and some anxious, all sharing the assumption that the goal of artificial intelligence is to achieve "human-level intelligence" or, as some predict, "superintelligence." "I don't care so much whether what we are building is real intelligence," says Peter Norvig, Director of Research at Google. "We know how to build real intelligence--my wife and I did it twice, although she did a lot more of the work. We don't need to duplicate humans. That's why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own."

What are the actual and potential applications of Machine Learning in Healthcare? Are you using Machine Learning and AI to the best of their capacities in your company? Is there anything else you should be doing? Do you have the right roadmap? During this master class you'll have the chance to learn, discuss and be inspired by Grant Allen, a Principal Data Architect in Google for 10 years, who will be visiting us from New York City.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree.

A new proof-of-concept study by researchers from the University of California San Diego succeeded in training computers to "learn" what a healthy versus an unhealthy gut microbiome looks like based on its genetic makeup. Since this can be done by genetically sequencing fecal samples, the research suggests there is great promise for new diagnostic tools that are, unlike blood draws, non-invasive. As recent advances in scientific understanding of Parkinson's disease and cancer immunotherapy have shown, our gut microbiomes – the trillions of bacteria, viruses and other microbes that live within us – are emerging as one of the richest untapped sources of insight into human health. The problem is these microbes live in a very dense ecology of up to 1 billion micobes per gram of stool. Imagine the challenge of trying to specify all the different animals and plants in a complex ecology like a rain forest or coral reef – and then imagine trying to do this in the gut microbiome, where each creature is microscopic and identified by its DNA sequence.

The exponential progress in the field of robotics has already been feared to take so many production jobs away from humans, and the latest edition to those victims might be the programmers. Researchers at the Google Brain artificial intelligence have designed a machine learning system that can develop machine learning software. Interestingly, when compared, it exceeded the results from the ones designed by humans. According to Jeff Dean, who leads the Google Brain research group, such exertion could supplant some of the work from the workers and enhance the pace of the implementation of the AI software in different fields of economy. "Currently the way you solve problems is you have expertise and data and computation," said Dean, at the AI Frontiers conference in Santa Clara, California.

In this paper, a progressive learning technique for multi-class classification is proposed. This newly developed learning technique is independent of the number of class constraints and it can learn new classes while still retaining the knowledge of previous classes. Whenever a new class (non-native to the knowledge learnt thus far) is encountered, the neural network structure gets remodeled automatically by facilitating new neurons and interconnections, and the parameters are calculated in such a way that it retains the knowledge learnt thus far. This technique is suitable for real-world applications where the number of classes is often unknown and online learning from real-time data is required. The consistency and the complexity of the progressive learning technique are analyzed. Several standard datasets are used to evaluate the performance of the developed technique. A comparative study shows that the developed technique is superior.

Self-learning, machine learning, and AI are all buzzwords in the tech field today. They all represent the next generation in software development and management. In this brave new world, programmers will often set up the application -- and the software will do the rest. Driven by big data, deep learning systems, and consumer demand, you may be investing in self-learning programs sooner than you think. Self-learning, often referred to as machine learning, is a form of AI.