Contrary to fictional portrayals of humans being herded like animals by Artificially Intelligent (AI) machines, realistic concerns about artificial intelligence are far more benign. Still, they are very important, and conversations have to begin in order to look at perhaps setting some ground rules. That's why the big players are stepping up. Researchers and scientists from large tech companies Google, Amazon, Microsoft, IBM, and Facebook have been meeting and discussing the future implications of AI for humans. While no hard details on the group's policies, objectives, or even it's name have come out, insiders have stated the group's intentions: to ensure that A.I. research is focused on benefiting people, not hurting them.

Amazon's voice-controlled speaker Echo and its Alexa software have been so successful that even rivals such as Google and reportedly Apple are developing competing versions. But Babak Hodjat, the inventor of the technology behind Apple's voice recognition system Siri, doesn't believe in Echo and Alexa's hype. He thinks they still lack one major component that makes human-machine conversations more complete: visual interactions. "It has to be augmented with all sorts of other visual cues, interactions, and maybe show some icons to allow the user to be able to click," Hodjat told Business Insider. "A solely speech recognition based system with no other user interface or contextual elements is incomplete."

Currently, developers use one of two methods to create speech programs. One involves using a large collection of words and speech fragments spoken by a single person, which makes sounds and intonations hard to manipulate. The other forms words electronically, depending on how they're supposed to sound. That makes things easier to tweak, but the results sound much more robotic. In order to build a speech program that actually sounds human, the team fed the neural network raw audio waveforms recorded from real human speakers.

In my last post, I showed how to find similar Game of Thrones episodes based on the characters that appear in different episodes. This allowed us to find similar episodes on an episode by episode basis, but I was curious whether there were groups of similar episodes that we could identify. A clustering algorithm groups similar documents together, where similarity is based on calculating a'distance' between documents. Documents separated by a small distance would be in the same cluster, whereas if there's a large distance between episodes then they'd probably be in different clusters. The KMeans algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares.

Intel has planted some solid stakes in the ground for the future of deep learning over the last month with its acquisition of deep learning chip startup, Nervana Systems, and most recently, mobile and embedded machine learning company, Movidius. These new pieces will snap into Intel's still-forming puzzle for capturing the supposed billion-plus dollar market ahead for deep learning, which is complemented by its own Knights Mill effort and software optimization work on machine learning codes and tooling. At the same time, just down the coast, Nvidia is firming up the market for its own GPU training and inference chips as well as its own hardware outfitted with the latest Pascal GPUs and requisite deep learning libraries. While Intel's efforts have garnered significant headlines recently with that surprising pair of acquisitions, a move which is pushing Nvidia harder to demonstrate GPU acceleration (thus far the dominant compute engine for model training) for deep learning, they still have some work to do to capture mindshare for this emerging market. Further complicating this is the fact that the last two years have brought a number of newcomers to the field--deep learning chip upstarts touting the idea that general purpose architectures (including GPUs) cannot compare to a low precision, fixed point, specialized approach.

Technology is continuing to play an integral part in sports. In cricket too, there are many areas where technology can be used. Machine learning will play an important role in Sports Analytics. We believe that we can use Machine Learning to analyze historical cricket games, and use this to continuously improve the Duckworth Lewis (D/L) Method of computing target scores in rain-shortened matches. The Current D/L method is a statistical method invented by statisticians Frank Duckworth and Tony Lewis.

Data analytics is becoming a cornerstone of the financial industry, with startups and established financial service firms looking to give investors clearer guidance with information collected and captured from multiple sources. Advances in machine learning and artificial intelligence (AI) in particular are providing greater insights and better customer experiences. AI-powered data analytics not only captures vast amounts of data in real-time, but also helps users understand how different data points relate to each other, providing insights that might otherwise be lost. Faced with a breakdown in brand loyalty as younger customers prioritize user experience, financial services are now racing to leverage data-driven cognitive technologies. Cambridge, MA-based Kensho, which recently received 58 million in funding from Goldman Sachs, San Francisco-based Alphasense, backed by Tribeca Venture Partners, and Toronto-based Bigterminal are some of the fintech players leveraging AI.

Manulife's Lab of Forward Thinking (LOFT) is undergoing efforts to accelerate its adoption of new technologies. In April, the Canadian insurer's U.S. unit John Hancock took a deep dive into blockchain--collaborating with startups ConsenSys and BlockApps. More recently, Manulife began exploring artificial intelligence and deep learning in its innovation labs through partnerships with Nervana Systems and indico data solutions. Once complete, the platform will serve as a means for research analysts to analyze and decipher natural language from public sources to make smarter investment decisions, the company said. Jesse Bean, LOFT's chief experience officer and VP, head of innovation, global solutions delivery spoke with INN about how the insurer is exploring deep learning and how the technology fits into Manulife's innovation strategy.

Artificial intelligence (or AI) has permeated most facets of our lives. Algorithms suggest our social media mates. But could the arrival of the robots be applied to education? Jozef Misik, managing director of Knowble, a language tech start-up whose products are built on AI, believes so: "Most educational technology products will have an AI or deep learning component in future," he says. Already, AI is able to address common learning challenges.

The commercial Internet has now been around for twenty some years and the overall experience hasn't changed much from the days of "You've Got Mail." The Internet started out as a research tool between government, universities and corporations. With the advent of hyperlinks, the Internet has been transformed into a commercial vehicle for the sale of good and services. The Internet of today as a research tool is pathetic and has taken on a bias of consumerism. Take this example: "show me all printers that use HP 950 Ink cartridges."