DeepMind, founded in the UK in 2010, created the first computer program to ever beat a professional at the game of Go (AlphaGo), created a DeepRL system to play Atari games at beyond human level performance (DQN), and is engaged in various research projects with the NHS to apply machine learning to radiotherapy planning for head and neck cancers and identification of conditions like age related macular degeneration in optical coherence tomography scans.

How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection. There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people's everyday lives. AI and decision-support systems are embedded in a wide array of social institutions, from influencing who is released from jail to shaping the news we see.

A number of companies are proving that, by incorporating some key user experience principles, creators can ensure that people will continue to use bots in 2017. For instance, The Horoscope Bot description (right) explains exactly what the bot has to offer. Copa Airlines' Ana bot is a virtual assistant that, "lets you ask questions using everyday language." Once a person sets up an Amazon Echo (via an app), they are presented with interesting and useful "skills" (Alexa's voice-activated versions of apps).

Editor's note: This guest column was written by Applause cofounder and general manager of emerging market products Roy Solomon. Bots are all the rage this year. Often called "conversational agents" or "dialog systems," researchers and programmers around the world are creating assistants that help people with tasks like with travel arrangements, finding food, live music and more. The biggest technology companies like Facebook, Google and Microsoft are heavily investing in bots. Despite the hysteria, bots are already viewed as a disappointment.

The scientific world is developing a knack for reading texts without opening them. Researchers in Israel and the US have conducted the first "virtual unwrapping" of a heavily damaged scroll, the En-Gedi scroll, to read its contents without destroying what's left. The team used a high-resolution volumetric scan to create a 3D model of the scroll, looked for bright pixels in the model (a sign of where the ink would be) and virtually flattened the scroll to make text segments readable. The process is slow, as you have to piece together segments and reconstruct lines of text that have been lost to the ages. However, the results were worth it in this case: the researchers discovered that this is the earliest known copy of a Pentateuchal book from the Bible (Leviticus) to be found in a Holy Ark, dating back "at least" 1,500 years.

Over the next decade, mobile, the Internet of Things, machine learning, robotics, and blockchain technologies will change a great deal about how the oil and gas industry works. Five technologies will change the oil and gas industry: mobile will speed oilfield transactions, increase efficiency, and improve safety by removing people from harm's way; the Internet of Things (IoT) will reduce the cost of repairs; machine learning will provide ever more optimal solutions to field challenges; robotics will upend the question of who does the work, and blockchain will make contracting faster and smoother than ever before. Adopting these technologies will be a challenge for many in our industry, requiring a change in mind-set. Engineers tend to focus less on investing for the future than on fixing what's broken now, as do companies trying to maximize their return on investment. But investments in these transformative technologies now will mean less to fix in the future, and more time to innovate, operate, and develop resources as fully as possible--which is what we're all trying to do, correct?

Today, the SEO world is abuzz with the term "relevancy." Google has gone well past keywords and their frequency to looking at the meaning imparted by the words and how they relate to the query at hand. In fact, for years, the common term used for working with text and language had been natural language processing (NLP). The new focus, though, is natural language understanding (NLU). In the following paragraphs, we want to introduce you to a machine-learning product that has been very helpful in quantifying and enhancing relevancy of content.

A rather high profile area generating headlines this year has been connected vehicles. The technological challenges that must be addressed before autonomous cars can be unleashed onto the streets are quite significant. Vision is one critical factor; your car needs to be able to identify all road hazards as well as navigating from A to B. So, how can a car achieve that in an often over-crowded highway space? Computer vision can be described as graphics in reverse. Rather than us viewing the computer's world, the computer turns around to look at ours.

Computer vision is by no means a new idea, there were automatic number plate recognition systems as early as the 1960s but deep learning is one of the key technologies that have expanded its potential. Early computer vision systems were algorithm-based, removing the color and texture of a viewed object in favor of identifying basic shapes and edges, and narrowing down what they might represent. This stripped back the amount of data that had to be dealt with and enabled the processing power to be concentrated on the essentials. Deep learning flipped this process on its head, instead of algorithmically working out that a triangle of certain dimensions was statistically probable to be a road sign, why didn't we look at a whole heap of road signs and learn to recognize them? Using deep learning techniques, the computer can look at hundreds and thousands of pictures, e.g., an electric guitar and start to learn what an electric guitar looks like in different configurations, contexts, levels of daylight, backgrounds and environments.

From health insurance selection to drug discovery, there's a huge potential for artificial intelligence algorithms to solve problems in the healthcare industry. Startups, big corporations, and investors alike are tapping into these opportunities. We tracked first fundings to AI-focused healthcare startups to gauge the volume of emerging investor-backed entrants in this space, and how the pace of these fundings varied over time. According to CB Insights data, around 20 new AI-centered healthcare startups raised their first equity rounds in 2016 alone and 50 have raised first equity rounds since the start of 2015. Our analysis includes the first equity round with a disclosed amount raised by a startup, including seed/angel, Series A, convertible note, and unclassified equity rounds raised by stealth companies like Imagen Technologies.