Though robots and artificial intelligence may not replace our doctors entirely in the foreseeable future, they are already starting to make a difference. Microsoft is now using machine learning and artificial intelligence to help doctors in India to diagnoze and treat eye diseases. Earlier this year, Microsoft began working with the not-for-profit LV Prasad Eye Institute (LVPEI) in India to have its Azure machine learning and Power BI services analyze patterns among cases and predict the surgical outcome of eye surgery patients. The collaboration saw Microsoft going through a trove of data -- anonymized records of 1.1 million people -- and provide doctors with insights into how the blindness spreads in the country, Anil Bhansali, Managing Director of Microsoft India (R&D), explained to Mashable India in a conversation. Microsoft says it utilized Azure machine learning service to crunch the numbers and Power BI service to visualize those numbers to make sense out of them.

Though robots and artificial intelligence may not replace our doctors entirely in the foreseeable future, they are already starting to make a difference. Microsoft is now using machine learning and artificial intelligence to help doctors in India to diagnoze and treat eye diseases. Earlier this year, Microsoft began working with the not-for-profit LV Prasad Eye Institute (LVPEI) in India to have its Azure machine learning and Power BI services analyze patterns among cases and predict the surgical outcome of eye surgery patients. The collaboration saw Microsoft going through a trove of data -- anonymized records of 1.1 million people -- and provide doctors with insights into how the blindness spreads in the country, Anil Bhansali, Managing Director of Microsoft India (R&D), explained to Mashable India in a conversation. Microsoft says it utilized Azure machine learning service to crunch the numbers and Power BI service to visualize those numbers to make sense out of them.

Louis Rosenberg thinks he has found a way to make us all a lot smarter. Rosenberg runs a Silicon Valley startup called Unanimous AI, which has built a tool to support human decision-making by crowdsourcing opinions online. It lets hundreds of participants respond to a question all at once, pooling their collective insight, biases and varying expertise into a single answer. Since launching in June, Unanimous AI has registered around 50,000 users and answered 230,000 questions. Rosenberg thinks this hybrid human-computer decision-making machine – once dubbed an'artificial' artificial intelligence – could help us tackle some of the world's toughest questions.

It's not all about the big boys, however, as NLP, text analysis and text mining technologies are becoming more and more accessible to smaller organizations, innovative startups and even hobbyist programmers. NLP is helping organizations make sense of vast amounts of unstructured data, at scale, giving them a level of insight and analysis that they could have only dreamed about even just a couple of years ago. Today we're going to take a look at 3 industries on the cusp of disruption through the adoption of AI and NLP technologies;

We study the complexity of the problem of searching for a set of patterns that separate two given sets of strings. This problem has applications in a wide variety of areas, most notably in data mining, computational biology, and in understanding the complexity of genetic algorithms. We show that the basic problem of finding a small set of patterns that match one set of strings but do not match any string in a second set is difficult (NP-complete, W[2]-hard when parameterized by the size of the pattern set, and APX-hard). We then perform a detailed parameterized analysis of the problem, separating tractable and intractable variants. In particular we show that parameterizing by the size of pattern set and the number of strings, and the size of the alphabet and the number of strings give FPT results, amongst others.

Recently we wrote about the development of AI and neural nets beyond the second generation Convolutional and Recurrent Neural Nets (CNNs / RNNs) which have come on so strong and dominate the current conversation about deep learning. Our research shows that the next generation of neural nets is most likely to be led by Spiking Neural Nets (SNNs) that are a return to the'strong' AI tradition and closely mimic actual brain function. Unlike CNNs that fire signals to every one of their deep layer connections every time, SNNs are modeled after the fact that in the brain neurons do not constantly communicate with one another. Rather they communicate in spikes of signals or more correctly short trains of spiking signals. As each spike in the train arrives at a neuron it raises the potential of that neuron until finally a spike arrives that tips it over its potential threshold and it in turn fires, propelling the signal onward.

As the global population ages, the number of cancer cases is going up. New cancer diagnoses are expected to rise by 70% in the next 2 decades, from 14 million to around 22 million, according to an estimate by the World Health Organization. Corporate giants like Google and IBM are already focusing on making breakthroughs in oncology, using advanced AI algorithms for early detection and personalized treatment of cancer. Google DeepMind announced a research partnership with the University College London Hospitals' radiotherapy department. DeepMind will test the use of machine learning to reduce the time it takes to plan radiotherapy treatment for hard-to-treat cancers of the head and neck.

Many current fears around AI and automation center around the idea that superintelligence could somehow "take over," turning streets around the globe into scenes from The Terminator. While there is much to be gained from discussing the safe development of AI, there's another more imminent danger: Autonomous weapons. On Friday, after three years of negotiations, the UN unanimously agreed to take action. At the Fifth Review Conference of the UN Convention on Certain Conventional Weapons, countries around the world agreed to begin formal discussions--which will take place for two weeks at the 2017 UN convention in Geneva--on a possible ban of lethal, autonomous weapons. Talks will begin in April or August, and 88 countries have agreed to attend.

Talis Capital has invested in Luminance. Luminance, of London, provides document analytics software for the legal industry. Investors include Invoke Capital and law firm Slaughter & May. London, 15 December 2016 – Luminance, a pioneer of artificial intelligence for the legal industry, has secured funding from Talis Capital, which values the company at over $20 million. Launched in September, with the backing of Invoke Capital and Magic Circle law firm Slaughter & May, demand for Luminance's product is growing rapidly, with the company recently completing its tenth data room project.

If one is looking for signals from an extraterrestrial civilization, why not practice on some of the non-human communication systems already known on our own planet? Whales have had a global communication system for millions of years--longer than Homo sapiens has even existed. Bees, which communicate in part by dancing, had democratic debates about the best places to swarm millions of years before humans came up with democracy as a political system. No person I know of who has studied another animal's communication system has ever concluded that the species was dumber than they'd previously thought. Through the study of animal communication, my colleagues and I have developed a new kind of detector, a "communication intelligence" filter, to determine whether a signal from space is from a technologically advanced civilization or not.