Yesterday morning I woke up, put on a pot of coffee, and checked the news. I wanted to revisit the New Hampshire primary results that had rolled in the night before. I opened Quartz's new app and was greeted with a text message: "Yep, it's really happening: Trump and Sanders won big in New Hampshire." Below it appeared side-by-side portraits of Trump's scowl and Bernie's grin. To read more, I tapped a ready-made text reply containing a donkey, an elephant, and an American flag emoji.

Tech giants are active players at the cognitive technologies scene. Alphabet and Facebook proved themselves active acquirers and investors in cognitive tech. Supercomputers, robots and drones are among themes approached by Internet giants. Microsoft, however, seems to be following a differed path: it integrates cognitive technologies into traditional products and makes them smarter. Bill Gates coined the concept of'digital nervous system (DNS)... that ... [provides] a well-integrated flow of information to the right part of the organization at the right time'.

Alphabet founders Larry Page and Sergey Brin are so enamored with the guy they put in charge of Google, CEO Sundar Pichai, that they turned him into an honorary founder and let him write their annual Founder's Letter this year. The Founder's Letter is traditionally when the founders spell out their vision for the company. But Page explained that he's so "pleased with Sundar's performance" and because "the majority of our big bets are in Google" that he decided to give Pichai "the bully-pulpit here" to talk about Google. A more cynical take would be that Page also wanted to sidestep talking about the controversies plaguing the other side of Alphabet, the so-called "Other Bets" division outside of the Google unit. As Business Insider's Jillian D'Onfro reports, although Alphabet makes 99% of its revenue from Google, mostly from its gargantuan ads businesses, it has been the "Other Bets" that have attracted the most media attention in recent months.

Artificial intelligence will likely not reach the level of intelligence that allows it to think like human beings, even if technology now is able to surpass humans in specific tasks, according to a senior Microsoft executive. "Artificial intelligence currently learns in a very controlled, monitored environment, it learns through data [given to it],"Rui Yong, assistant managing director at Microsoft Research Asia, said in an interview with the South China Morning Post. "But that is not how humans learn." While artificial intelligence technology today has surpassed human ability in certain tasks, such as AlphaGo's ability to beat humans in the game of Go or Microsoft's computer vision technology that recognises objects in images more accurately than humans, these technologies are tailored to achieve specific tasks and cannot adapt quickly to new problems. "When humans encounter a new situation, we can adapt and use our imagination to find a solution. But for computers, if they have never encountered a certain problem, then they cannot solve it," he said.

DUBLIN--(BUSINESS WIRE)--Research and Markets has announced the addition of the "Artificial Intelligence and the Agricultural Industry" report to their offering. The world population will grow from 7.3 billion people today to 8.2 billion by 2025, and 9.7 billion by 2050. As a society, we will need to produce 70% more food than is currently produced today. The agricultural Industry requires a revolution and artificial intelligence is that revolution. Artificial intelligence will drive five use cases that will enable the industry to balance the need to produce more food and the need to reduce environmental impact.

A new tech startup is merging deep learning with autonomous cars. Drive.ai has become the 13th company to be granted a license to test autonomous cars on public roads in California. If you haven't yet heard of them, you're not alone. Drive.ai has been in stealth mode for the past year. However, the company recently closed 12 million in Series A funding while developing deep learning technologies with their team of experts who specialize in everything from natural language processing, computer vision, and autonomous driving.

On yesterday's investor call, Facebook founder and CEO Mark Zuckerberg was asked how the machine learning technology behind its recent introduction of bots to Messenger would manifest itself in the future. "So the biggest thing that we're focused on with artificial intelligence is building computer services that have better perception than people," he replied. "So the basic human senses like seeing, hearing, language, core things that we do. I think it's possible to get to the point in the next five to 10 years where we have computer systems that are better than people at each of those things." There are a couple of striking things about this statement: it imagines computer systems with abilities that are central to our existence as biological creatures, the essential skills that we rely on to understand and interact with the world.

With the big data growing bigger and bigger and social media penetrating every facet of the society, construing and monitoring data is one of the biggest challenges faced by the enterprises. Gone are those days when customers have to lodge a formal complaint to register the malfunctioning of any product/services provided by the business enterprise, rather, users these days take it to the social media forum to express their dissatisfaction and anguish towards any improper services/products. Inputs such as tweets, facebook comments could be of significant value to the enterprise to analyze their products/services/ performances, customer behavior and demands. Below is a small case study on Flipkart and Snapdeal performance when'World Book Day' was trending on Twitter. Below is the screenshot of'Flipkart' and'Snapdeal' on the occasion of'World Book Day'.

Inside Science Minds presents an ongoing series of guest columnists and personal perspectives presented by scientists, engineers, mathematicians, and others in the science community showcasing some of the most interesting ideas in science today. In fact, upon reflection, it was amazing how often the word "algorithm" came up in the course of our conversations with these accomplished scientists. The boom in software and computing has achieved powerful and profound results in our society. And, yes, the world is a better place, thanks to data analytics. But we need to slow down and regain our perspective, because Big Data and machine learning are absolutely not ends unto themselves, and they certainly aren't a replacement for basic scientific research and exploration.

Recurrent neural networks (RNN) are simple dynamical systems whose computational power has been attributed to their short-term memory. Short-term memory of RNNs has been previously studied analytically only for the case of orthogonal networks, and only under annealed approximation, and uncorrelated input. Here for the first time, we present an exact solution to the memory capacity and the task-solving performance as a function of the structure of a given network instance, enabling direct determination of the function–structure relation in RNNs. We calculate the memory capacity for arbitrary networks with exponentially correlated input and further related it to the performance of the system on signal processing tasks in a supervised learning setup. We compute the expected error and the worst-case error bound as a function of the spectra of the network and the correlation structure of its inputs and outputs.