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.

Investors poured a record high 1.05B into artificial intelligence startups in Q2'16, and AI is already affecting more areas of our lives than many people realize. Even retail and e-commerce companies are increasingly integrating the technology. Recently there's been a rush of AI announcements and acquisitions by major retailers: Just this week, Etsy acquired Blackbird to enhance its search functionality through AI, followed the very next day by Amazon acquiring Angel.ai And earlier this month, e-commerce unicorn Houzz (see our full unicorn tracker here) announced a deep learning initiative to help users find and buy products by clicking on images. Using CB Insights data, we dove into the wide array of AI startups focused on retailers and e-commerce businesses, including AI-powered personal shopping apps, natural language processing and image recognition tools for shopping websites, predictive inventory allocation tools, and more.

For Matt Osborne, finding exploited children typically starts with a walk on the beach, and it ends with hands cuffed behind his back. It's almost always the same--Osborne and a few friends travel somewhere that's known for sex tourism and walk along the beach or hang in area nightclubs, not to look for girls but to be seen themselves. A group of white American men is easy to spot in heavily-touristed resort towns in Asia, Central America, and South America, so it doesn't take long to make a connection. "They approach us," Osborne says. "At first, everything is innocuous. Want to go jet ski or parasailing? Buy a margarita or beer? They offer us drugs, and the conversation always turns to girls. And if you let them talk long enough and say, 'What else do you have? What else do you have?' Then sooner or later, they always offer us young girls."

I have a little secret: I don't like the terminology, notation, and style of writing in statistics. I find it unnecessarily complicated. This shows up when trying to read about Markov Chain Monte Carlo methods. Take, for example, the abstract to the Markov Chain Monte Carlo article in the Encyclopedia of Biostatistics. Markov chain Monte Carlo (MCMC) is a technique for estimating by simulation the expectation of a statistic in a complex model. Successive random selections form a Markov chain, the stationary distribution of which is the target distribution.