Ask most people outside academia or Silicon Valley what comes to mind when they hear the term "machine learning" and you're likely to get a response that involves a movie like "The Matrix" or "Ex Machina." You're less likely to hear how it's a great tool for fraud detection or supply chain optimization, and that's too bad. Machine learning has a tremendous range of business applications, from optimizing data centers to predicting fine wine price changes to retail market basket analysis. With that in mind, I hope to cut through the science fiction clutter and misconceptions so you can consider how machine learning relates to your business. Many have heard about Andrew Ng's popular graduate level machine learning course at Stanford, now available on Coursera.

Some time in earlyFacebook will be'better than humans' in a DECADE, Mark Zuckerberg claims. Read more ... » 2016, Siddhartha Pahwa thought it would be "disruptive" if he could make a softwareAutomation of the creative: Are the bots coming for our creative and marketing departments?. Read more ... » that will allow users to book a cab on WhatsApp. That would give Pahwa's Meru Cabs a technologicalMan and machine, human and bot. Read more ... » edge over other cab aggregators such as Uber and Ola, but to his disappointment WhatsApp did not allow automatedAutomation of the creative: Are the bots coming for our creative and marketing departments?.

IMAGE: UMass Amherst researchers will apply recent advances in machine learning, specifically biologically inspired deep learning methods, to analyze large amounts of scientific data from laser-induced breakdown spectroscopy and hyperspectral camera... view more They are funded by a new four-year, 1.2 million National Science Foundation grant to computer scientist Sridhar Mahadevan, lead principal investigator at UMass Amherst's College of Information and Computer Sciences. His co-investigators are Mario Parente, an expert in analysis of hyperspectral images at UMass Amherst, and Darby Dyar of Mount Holyoke, a specialist in planetary chemistry and geology who serves on the scientific mission team for the Mars rover. As Mahadevan explains, NASA's Curiosity rover, a car-sized robot, has been exploring a crater on Mars since August 2012 and sending back a steady stream of specialized camera images and data on the chemical composition of rocks and dust for analysis. The data range from one-dimensional spectra of rock samples to three-dimensional hyperspectral images of the Martian surface. He advises Ph.D. students Thomas Boucher, CJ Carey, Steve Giguere, Ian Gemp, Francisco Garcia and Ishan Durugkar in the Autonomous Learning Laboratory, who are exploring machine learning methods to show, for the first time, that new deep learning approaches provide a practical and useful new tool for handling large scientific data sets.

The unstoppable march of the machine passed a significant milestone last month when Google Deepmind's AI AlphaGo program beat a world grandmaster at the ancient game of Go. This feat had not been expected to be achieved for several years. The victory wasn't from a machine that was able to beat the human by sheer'brute force' – crunching a huge amount of possible outcomes in a fraction of a second (which is how IBM's Deep Blue conquered chess). AlphaGo won by using intuition; playing itself at the game millions of times and learning from its mistakes. The result has sent shockwaves through the world of technology, and it has also raised the spectre of AI replacing humans in the workplace.

Artificial Intelligence (AI) is being considered as the next technology evolution, which would disrupt a large chunk of jobs performed by human beings. Learning machines, which are being designed, are likely to learn faster than human beings. Consequently, they could even turn out to be our foes, or pose as survival threats to human beings. Not surprising though, a host of important personalities, such as Bill Gates of Microsoft, Elon Musk of Tesla, and Stephen Hawking, the famous physicist of our times, have warned us about the serious consequences that AI would cause to the human race. The last few years have witnessed lot of work, and implementation in the automation space, particularly in Artificial Intelligence.

Recently, we have seen artificial intelligence triumph over humans in Jeopardy and chess. And there is a growing presence of virtual assistants like Alexa, Cortana, and Siri that populate our computers, phones, and homes. It's only a matter of time before A.I.-powered assistants play a significant role in the workplace, experts say. In fact, the global intelligent virtual assistant market is forecast to be worth 5.1 billion by 2022, up from - 600 million in 2014, according to Transparency Market Research. What are the potential benefits and challenges of giving smart virtual assistants a home in the enterprise?

I've looked before at the growing role AI is playing in the development of new medicines, whether it's understanding which compounds to test, or even in the creation of virtual models to test drugs in. At the forefront of this trend is Insilico Medicine, who you may remember I wrote about recently after they'd developed a system that can guess your age accurately just by looking at you. They have certainly been busy, and recently published a paper looking at the role of deep learning in predicting the impact drugs might have on the body. The study saw a neural network trained up to predict the therapeutic use of a huge array of drugs. The team measured the differential signaling pathway activation score for a wide range of different pathways to reduce the deminsionality of the data, whilst ensuring that it remained scientifically relevant.

Every few years a new movement becomes transformational in the technology industry driving a brand new generation of startups and new businesses. Some of those movements can be technological in nature like social, mobile, cloud or IOT while others like crowdsourcing or the gig economy are more transformational from the social standpoint. Each one of those movements creates many companies that attempt to leverage its principles in a specific domain. How many "Facebook for X", "AWS for X" or "Uber for X" have we seen in the last few years. These days, the new Machine learning and artificial intelligence are capturing the imagination of the technology industry.

Straight from the horse's mouth, this essential guide is written by the language's creator.

Speechmatics Auto-Auto responds to market demands for new languages. Speechmatics' recent release of the Auto-Auto framework continues to create exciting new capability for the Speechmatics platform, delivering German to our line-up of new languages. Neil MacDonald, Chief Revenue Officer, commented "German represents the latest in a continuous and accelerating delivery roadmap of language capability. This has only been made possible through our unique Machine Learning training platform that delivers new languages, revisions and specific use-case variants, using minimal data in as little as 10 days." In laying out our future roadmap plans, we are now allowing our customers and partners to help us decide which languages to develop next.