At Curalate, we're fascinated by the future of computer vision and machine learning. Those technologies have made serious strides over the past few years and the future looks incredibly bright. Curalate is doing its part to define how artificial intelligence meets commerce with Intelligent Product Tagging -- technology that can analyze an image and use machine learning to identify the products depicted within that image. For example: If you have a photo of a woman wearing a floral dress, our technology can identify that dress, then visually match it with the corresponding product in a brand's catalog -- making the image shoppable. We expect to start introducing this tool to clients in 2017, but it's actually a much longer-term research effort for Curalate's talented product development team.

The clinical, emotionless computer or robot is a staple of science fiction, but science fact is starting to change: computers are getting much better at understanding emotions. As we turn to computers, smart devices and robots to do more and more functions that have always been the exclusive domain of humans, this emotion-detecting technology will become increasingly important. Automated customer service "bots" will be better able to know if a customer is getting the help they need. Robot caregivers involved with telemedicine may be able to detect pain or depression even if the patient doesn't explicitly talk about it. One insurance company I am working with is even experimenting with call voice analytics that can detect that someone is telling lies to their claims handers.

With Version 6.0, Kinetica introduces user-defined functions (UDFs), enabling GPU-accelerated data science logic to power advanced business analytics, on a single database platform. User-defined functions (UDFs) enable compute as well as data-processing, within the database. Such'in-database processing' is available on several high-end databases such as Oracle, Teradata, Vertica and others, but this is the first time such functionality has been made available on a database that fully utilizes the parallel compute power of the GPU on a distributed platform. In-database processing in Kinetica creates a highly flexible means of doing advanced compute-to-grid analytics. This industry-first functionality stands to help democratize data science.

This heightened interest in AI is also reflected in the sheer volume of popular fiction works such as Wetsworld, HumansandEx Machina, which deal with the moral dilemmas of autonomous robots and thinking machines. Yes as much as we're fascinated by these (as yet) fictional scenarios, many still struggle to grasp exactly how this technology will – and in many cases already does – affect our everyday lives. For businesses this has become an imperative, however, and we have seen the focus of Big Data become much more commercially-oriented, centring on managing, measuring and monetizing so-called information assets. To secure an advantage in this data-driven landscape, organisations must develop real world solutions and applications with big data analytics that impact their bottom line. The CRM industry, for instance, has taken to artificial intelligence in a big way over the past year, with companies such as Salesforce, Oracle and Base developing tools to drive sales interactions through built-in intelligence. Personalization is one way in which that translates into tangible commercial impact, as it enables companies to scale their services without incurring prohibitive costs or compromising quality.

There have been numerous examples over the last two decades of how Natural Language Processing, or NLP, is being used by companies to provide an intelligent voice to gadgets and searches. Think, for instance, how the world of search engines--from Yahoo, Microsoft and Google--have changed the Internet with text-based search algorithms driving and augmenting the World Wide Web. NLP, though, does much more than just that and text analytics. NLP exploration on our current digital planet includes voice searches on automobiles and then, of course, the dictation mechanics of the software world. I have an 18-month-old who thrives on YouTube searches asking for'Peppa Pig' or'Mickey Mouse' series while my 5-year-old is exploring the world of content on YouTube (of course, with restricted parental control)--from watching the world of KungFu Panda to how to make dummy videos.

Machine learning is a rising star in the compute constellation, and for good reason. It has the ability to not only make life more convenient – think email spam filtering, shopping recommendations, and the like – but also to save lives by powering the intelligence behind autonomous vehicles, heart attack prediction, etc. While the applications of machine learning are bounded only by imagination, the execution of those applications is bounded by the available compute resources. Machine learning is compute-intensive and it turns out that traditional compute hardware is not well-suited for the task. Many machine learning shops have approached the problem with graphics processing units (GPUs), application-specific integrated circuits (ASICs) – for example, Google TensorFlow – or field-programmable gate arrays (FPGAs) – for example, Microsoft's investment in FPGAs for Azure and Amazon's announcement of FPGA instances.

Artificial intelligence is one of the hottest technology trends for 2017. And perhaps no company in the AI sector is hotter than NVIDIA, which has pushed from the desktop into the data center, evolving into a major player in high performance computing. NVIDIA's graphics processing (GPU) technology has been one of the biggest beneficiaries of the rise of specialized computing, gaining traction with workloads in supercomputing, artificial intelligence (AI) and connected cars. This trend is expected to accelerate in 2017, with more custom chips being introduced to target these workloads. After building a major beachhead in hyperscale data centers, NVIDIA's ambitions now extend to the enterprise data center. The company's new DGX-1 Deep Learning System is a "supercomputer in a box" – a hardware appliance designed to make AI data crunching more accessible.

Many technologies follow a'hockey stick' curve. A great number of them seem to be around for years, they are talked about within the IT industry, white papers are written about them, but it takes a long time for them to actually reach the threshold of public consciousness. This is certainly true for neural networks. This computational approach, loosely modeled on the way a biological brain solves problems, has at least in theory been discussed since as early as the 1940s. It is only recently, however, that neural networks are not only becoming a reality, but also finding real, value adding use cases.

In yet another episode of man versus machine, an artificial intelligence developed by Carnegie Mellon University has been absolutely dismantling a team of professional poker players, accumulating a staggering lead of almost $800,000. The showdown takes place as part of the "Brains vs. Artificial Intelligence" competition which pits a group of four poker pros against the crafty supercomputer Libratus in a heads-up game of No-Limit Texas Hold'em slated to continue for 120,000 hands. Last year, Facebook's VP of Design thought the TNW Conference main stage was the best she'd ever been on. Since January 11 when the contest initially kicked off, the players have now passed the midway point of the the race, having completed almost 65,000 hands in total. What is more intriguing is that so far Libratus has managed to keep an impressive lead over its human opponents, stacking up a profit of $794,392.

Last year I shared my collection of video resources for machine learning. It was unwieldy taking individual e-mail requests and updating a time-stamped blog post.