Amazon is partnering up with Techstars to launch an Alexa Accelerator for companies developing conversational artificial intelligence. Starting in January 2017, early-stage companies working on speech technology or voice applications in any industry--including, but not limited to automobiles, health, communications, wearables, and connected homes--can apply for the 13-week program, and a chance to win $120,000. Ten to 12 startups will have a spot in the program, and will live at the University of Washington beginning in July 2017. Participants will each receive a total of $20,000, and will have a chance to work with Amazon and Techstars mentors to develop strategies that will help further their business. A demo day will be held in October during which companies will share their product with investors, and a select few will win an additional $100,000 at the conclusion of the program.

The mainstream media is trying to make it look like Donald Trump has no chance of beating Hillary Clinton on Election Day in two weeks. That's why it came as a massive shock to many when an artificial intelligence (AI) system that correctly predicted the last three elections puts Trump ahead of Clinton in the race. According to The Gateway Pundit, the system found that enthusiasm for Trump is higher than the numbers Barack Obama had in 2008. Trump has overtaken Obama's popularity that year by a whopping 25%. CNBC reported that the system is called MogIA, and it was founded by Sanjiv Rai, the founder of Indian start-up Genic.ai.

What can your face say about you? Face recognition technology can pick up on things like your age, gender and maybe even your mood. Now, two researchers say it could even tell whether you're a criminal. They are claiming to have developed a system that, when shown a series of faces it has never encountered before, can pick out the ones belonging to convicted criminals. But other researchers have criticised the results, and say the work raises ethical questions over what face recognition technology can and should be used to detect.

Machine learning has become an invaluable tool in the fight against fraud. It combines computational statistics, artificial intelligence, signal processing, optimisation, and other methods to identify patterns. Machine learning has been a significant breakthrough in helping companies move from reactive to predictive by highlighting suspicious attributes or relationships that may be invisible to the naked eye but indicate a larger pattern of fraud. The great value of machine learning is the sheer volume of data that computers can analyse that humans cannot, thanks to a variety of pattern recognition algorithms. With this you can add exponentially more data to your analysis -- but selecting the right data and approach to model the problems is critical. A solid solution also requires specialised expertise to apply rigorous methodology in data analysis and develop the fraud models to ensure consistent quality.

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Almost a year ago, I wrote these words, "Technology has reached the tipping point for me, it moved from a help to a hindrance." The plethora of adrenaline- and endorphin-inducing mobile apps, 24x7 news, notifications, alerts and updates, drip fed my brain and hindered my "deep work and deep thoughts." In Cal Newport's new book titled, Deep Work he posits that most knowledge workers need concentration and substantial time, dedicated and uninterrupted, to produce their best work. He argues that a lot of technologies and open office layouts today inhibit creativity, "deep work" and "deep thoughts," and are the very things that are most highly valued, and one of the key differentiators between humans and robots. Newport argues that we must understand and optimize the conditions that enable our brains to work best. To sum up his argument, constant drip feeding technologies serve to prevent deep thoughts and deep work, our most valuable assets.

Businesses that use artificial intelligence (AI), big data and the Internet of Things (IoT) technologies to uncover new business insights "will steal $1.2 trillion per annum from their less informed peers by 2020." So says Forrester in a new report, "Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution." Across all businesses, there will be a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. Through the use of cognitive interfaces into complex systems, advanced analytics, and machine learning technology, AI will provide business users access to powerful insights never before available to them. It will help, says Forrester, "drive faster business decisions in marketing, ecommerce, product management and other areas of the business by helping close the gap from insights to action."

With the advent of photography, a tiny fraction of 19th-century scientists believed they could develop methods of accurately identifying criminals by their facial features. While their hypotheses were eventually discredited, new artificial intelligence technology suggests their claims might've been valid after all. Xiaolin Wu and Xi Zhang from Shanghai Jiao Tong University in China have resurrected this facial recognition tradition and built a neural network that can supposedly pick out criminals by simply looking at their faces. TNW Conference is back for it's 12th year. To accomplish this, the researchers used an array of machine-vision algorithms to examine a series of facial juxtapositions between photos of criminals and non-criminals with the goal of finding out whether a neural network can reliably tell them apart.

As tech giants make bigger leaps into artificial intelligence, they see marketers as key customers for a variety of services and are promising more nuance and layers than the technology's early days might suggest. Companies such as IBM, Salesforce and others believe AI has the potential to transform marketing as much as the digital revolution has over the past several years. IBM, which first made an AI media splash in 1997 when its Deep Blue supercomputer beat chess champion Garry Kasparov, has emerged as an early market leader with newer, smarter Watson software. Google is also ramping up R&D spend on AI; Microsoft is integrating AI across its enterprise; Facebook is building out facial recognition AI for basic user experiences and Salesforce is championing a new platform called Einstein to help optimize for individual customer data. In late September, all of these companies and others banded together to create the Partnership on Artificial Intelligence to Benefit People and Society, a group with the objective of addressing opportunities and challenges, along with standards and best practices, for AI developers.