IBM has unveiled Project Debater, a conversational artificial intelligence which can hold a debate using information from journals and newspaper articles. Project Debater began its training six years ago but it could only hold a debate with people two years ago. A demo was held for journalists at IBM's offices in San Francisco where the AI debated issues relating to healthcare and the subsidisation of space exploration. The company didn't give its AI an easy time either – it was competing against Dan Zafrir, President of the International Debate Society in Israel, and 2016 national Israel debate champion Noa Ovadia. Debater opened its supporting argument for space exploration subsidisation with facts such as how it benefits humankind because it helps advance scientific discoveries and inspires young people to think beyond themselves. Ovadia countered the argument saying there are better applications for government subsidies, including for scientific research here on Earth.

As long as there have been personal computers, there have been people yelling at them. And before that, angry couch potatoes argued with their TV sets. Now artificial intelligence technology that IBM has been developing over the past six years can give the machines a chance to argue back. The company took the wraps off an AI system Monday that it calls Project Debater, staging a couple of debates between it and experienced human debaters that show how far its capabilities have come. IBM, which has been staging in-house debates between humans and Project Debater for a few years, said the demonstration represented the first such public debate.

Today, an artificial intelligence (AI) system engaged in the first ever live, public debates with humans. At an event held at IBM's Watson West site in San Francisco, a champion debater and IBM's AI system, Project Debater, began by preparing arguments for and against the statement: "We should subsidize space exploration." Both sides then delivered a four-minute opening statement, a four-minute rebuttal, and a two-minute summary. Project Debater made an opening argument that supported the statement with facts, including the points that space exploration benefits human kind because it can help advance scientific discoveries and it inspires young people to think beyond themselves. Noa Ovadia, the 2016 Israeli national debate champion, opposed the statement, arguing that there are better applications for government subsidies, including subsidies for scientific research here on Earth.

People who want to be inspired watch TED Talks, largely because of the stories they tell. These stories spark imagination and motivate audiences to think in new ways. TED Talks redefine knowledge and point to potential. Tufekci explains how we are routinely using computers to make subjective decisions like hiring new employees, releasing criminals from prison, and even identifying handwriting. Machine learning has made it possible for computers to make decisions about things we might not have disclosed – for example, about future likelihood of depression or pregnancy.

This week saw a lot of Greater Bay Area announcements with electronic border crossings powered by WeChat, one of the world's largest logistics hubs powered by Alibaba and artificial intelligence powering Chinese courtrooms. China is pushing the boundaries of technology more than any other country and This Week In China Tech is the best way for you to stay on top of the stories that matter most. Tencent's WeChat E-Pass To Allow Seamless Border Crossing In The Greater Bay Area China's Greater Bay Area is one of the country's most important strategic initiatives, connecting 11 cities and 67 million people into the world's first true "megacity." The Greater Bay is focused on rivaling America's Silicon Valley, but one of the largest challenges has been the multiple borders between Hong Kong, Macau, and Guangdong. Enter Tencent, China's version of Facebook, with their WeChat E-Pass that will allow their 1 billion users with Chinese IDs to link their IDs to their cell phone and cross these three borders seamlessly (article in Chinese).

Not a week goes by that I don't see dozens of headlines blaming "AI" or "deep learning" for yet another ethical conundrum that will doom human society. Whether it is predictive policing or facial recognition or autonomous weapons, it seems nearly every facet of society is facing an "AI revolution" that will destroy humankind. If one peels back the breathless hype and viral buzzwords, however, is it really AI that we are worried about or is it the shift towards a data-centric society with or without deep learning advances? To the general public, "big data" and "deep learning" are increasingly becoming synonymous, fueled by the never-ending hype machine of Silicon Valley and the very legitimate advances occurring in deep learning powered largely by the massive availability of large datasets and the unique abilities of those models to make sense of all that data. From a technical standpoint, however, these are two entirely distinct concepts.

Bobby Kasthuri has a problem. In an effort to understand, on the finest level, what makes us human, he's set out to create a complete map of the human brain: to chart where every neuron connects to every other neuron. The problem is, the brain has more connections than the Milky Way has stars. Just one millionth of the organ contains more information than all the written works in the Library of Congress. A map of the brain would represent the single largest dataset ever collected about anything in the history of the world. Making that map seems like a task that could consume not just one lifetime, but dozens.

No industry counts more than healthcare and medicine. And I don't think I should explain'why'. So whatever advancements we may have done in any industry till today with Artificial Intelligence or with any other cutting edge technologies for that matter, our healthcare and medicine should come top in that list and there should not be any disagreement on this. Let's do a reality check and see how far Artificial Intelligence has already been leveraged in our healthcare and medicine. When you go to your doctor today, your doctor can't guarantee that a particular treatment selected for you is going to work for sure.

On a stage in San Francisco, IBM's Project Debater spoke, listened and rebutted a human's arguments in what was described as a groundbreaking display of artificial intelligence. The machine drew from a library of "hundreds of millions" of documents - mostly newspaper articles and academic journals - to form its responses to a topic it was not prepared for beforehand. Its performance was not without slip-ups, but those in attendance made clear their thoughts when voting on who did best. While the humans had better delivery, the group agreed, the machine offered greater substance in its arguments. That, IBM said, spoke to the heart of its goal: augmenting human beings to make decisions quickly and with more data than ever before.

Bangalore: Artificial intelligence (AI) is the next big step for retail and FMCG companies following the implementation of advanced big data and analytics (BDA) solutions, which can unlock huge data volumes in an automated way in real-time and ultimately lead to maximum profitability, according to GlobalData. The company's Disruptor Database revealed that although retailers have grown to their current size by capitalizing on profitable bits of digitalization, they are often challenged in understanding what their customers need at scale. There is a colossal amount of customer data with enterprises, but only a handful are able to generate value deemed from low conversion rates of below 5% overall. This broadens the scope for retailers and consumer goods companies to use predictive analytics to enhance decisions related to supply chain management, customer behavior, staff allocation and the likelihood of goods being damaged, lost or returned. "While most analysis related to understanding market trends, achieving greater customer personalization, and improving operational efficiency can be performed with BDA methods, AI in many cases is less cost intensive and faster - at times even instant.. Intelligent machine learning systems can replace expensive armies of data scientists and provide solutions or product recommendations in an automated way. The timing of the analytics is crucial, since opportunities for cost savings or additional sales are frequently limited to minutes or even seconds," said Rena Bhattacharyya, Technology Research Director - GlobalData.