I was playing around with Algorithmia for a story about their business model back in December, when I got sucked into playing with their DeepFilter service, resulting in a 4-week long distraction which ultimately became what I am calling my algorithmic rotoscope work. After weeks of playing around, I have a good grasp of what it takes to separate videos into individual images, applying the Algorithmia machine learning filters, and reassembling them as videos. I also have several of my own texture filters created now using the AWS AMI and process provided Algorithmia -- you can learn more about algorithmic rotoscope, and details of what I did via the GitHub project updates. The project has been a great distraction from what I should be doing. After the election, I just did not feel like doing my regular writing, scheduling of Tweets, processing of press releases, and the other things I do on a regular basis.

How do we unlock the true value of data? Nowadays, collecting data has never been easier and faster, but understanding its true meaning and value still remains a challenge. IBM estimates 90% of all the data existing in the world today has been generated in the last 24 months. For data-driven organisations such as the NHS, which has disparate trusts throughout Britain generating vast amounts of data, having the ability to harness this information and analyse it correctly to get the right insights has never been more important. In the technology profession, we may have, ironically, promoted Big Data as a panacea without truly and fully understanding the issues it can solve.

To solve real-world problems with AI, a deep learning system would need to be trained on a trillion parameters in 20 minutes. Even Intel is willing to admit that computers are great at crunching numbers, but not so great that they also make good decision-makers. Based on a recent webinar about the hardware advancements that have made better artificial intelligence (AI) possible, and what the future holds, that is about to change, and much faster than many would believe. Pradeep Dubey, the director of the Parallel Computing Lab at Intel, explained the difference between traditional AI systems and newer implementations like deep learning--primarily, it's about who is making the rules. In traditional AI, humans have to create rule-based systems for understanding which data should be processed, and how.

Advancements in machine learning and artificial intelligence (AI) opens new doors for businesses to make data-informed decisions, and if your business does not take advantage of the growing industry of data analytics, your competitors will steamroll ahead. The advancement of machine learning and artificial intelligence is opening new doors for businesses to make more data-driven decisions at higher accuracy rates. Data and analytics are also changing the way that businesses compete. As leading companies take advantage of the power of big data, and laggards stay behind, the disparity between the two groups will continue to widen in the future. Recently, Forbes has predicted that by 2020, we will have 1.7 megabytes of new information created every second for every human everywhere on the planet.

Whenever one separates sociology and technology, one misses the point.

Chatbots have not seen the success in non-spamming application that we had once hoped. Microsoft's Tay experiment was a massive failure thanks to human interference. Microsoft is trying again with Zo, focusing on the future of chatbots being your best friend. That future is fluid interaction with a programmable and learning artificial intelligence without the assistance of humans. Conversely, In the case of Sensay, the future of bots is one that is just as human as a bot could possibly be.

Major AI researchers and textbooks define the field as "the study and design of intelligent agents", where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as "The science and engineering of making intelligent machines". AI research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues.

President-elect Trump has met with leaders in technology in an effort to open lines of communication and discuss business after months of two-way criticism. It's no secret that Silicon Valley was largely in support of Hillary Clinton, who had aligned herself with the technology community, while over the last few years Mr. Trump has criticized Apple's iPhones, accused Facebook, Google, and Twitter of burying negative news about Democrats, and picked a fight with Jeff Bezos on Twitter insinuating his purchase of the Washington Post was for political influence to help Amazon's business. While this meeting simply serves to smooth over relations with the technology community, there is a longer conversation needed with President-elect Trump, who's presidency sits at a tipping point in technology. In the next four years we will see an explosion of AI technology that further delivers on the promise of driverless cars, intelligent robots, and other societal and job-impacting advancements. The conversation needed is how to, or more precisely, how not to regulate AI.

At CES 2016, Microsoft announced that it is building a platform to enable automobile manufacturers to deliver connected car experiences to customers. Microsoft Connected Vehicle Platform is a set of services that is integrated with Azure, Cortana, Office 365, Power BI, and Skype. In the last couple of years, Microsoft has doubled down its investment in machine learning (ML) and analytics. The company is embedding ML in almost all its products. Powered by Azure, these new set of services such as Azure ML, Microsoft Cognitive Services, Cortana Intelligence Suite takes advantage of the abundant compute and storage resources exposed by the underlying infrastructure.

Amazon Echo is apparently always ready, always listening and always getting smarter. So goes the spiel about the sleek, black, voice-controlled speaker, Amazon's bestselling product over Christmas, with millions now sold worldwide. The problem is that when you have Alexa, the intelligent assistant that powers Amazon Echo, entering millions of homes to do the shopping, answer questions, play music, report the weather and control the thermostat, there are bound to be glitches. And so to Dallas, Texas, where a six-year-old girl made the mistake of asking Alexa: "Can you play dollhouse with me and get me a dollhouse?" Alexa promptly complied by ordering a $170 (£140) KidKraft doll's house and, for reasons known only to the virtual assistant, four pounds of sugar cookies.