Earlier this year, the president included in his fiscal 2017 budget proposal a 4 billion request to make computer science a new "basic skill." The Education Department is prioritizing coding programs through a new experiment that allows students to tap federal financial aid to help pay for them. The private sector is prioritizing the issue as well, pouring hundreds of millions of dollars into increased access to computers, computer science and broadband internet. Even toy manufacturers are getting in on it, dreaming up toys aimed at teaching children as young as 3 to think like a computer coder.

On the island of Santorini, Greece, a group of AIs has been facing off in an epic battle of Doom. This is VizDoom, a contest born from one man's idea: To improve the state of artificial intelligence by teaching computers the art of fragging. That simple notion then spiraled into a battle between tech giants, universities and coders. Over the past few months they've all been honing their bots (known as "agents"), building up to one, final death match. Okay, it was a lot more than one match.

IBM Research announced a multi-year collaboration with the Department of Brain & Cognitive Sciences at MIT to advance the scientific field of machine vision, a core aspect of artificial intelligence. The new IBM-MIT Laboratory for Brain-inspired Multimedia Machine Comprehension's (BM3C) goal will be to develop cognitive computing systems that emulate the human ability to understand and integrate inputs from multiple sources of audio and visual information into a detailed computer representation of the world that can be used in a variety of computer applications in industries such as healthcare, education, and entertainment. The BM3C will address technical challenges around both pattern recognition and prediction methods in the field of machine vision that are currently impossible for machines alone to accomplish. For instance, humans watching a short video of a real-world event can easily recognize and produce a verbal description of what happened in the clip as well as assess and predict the likelihood of a variety of subsequent events, but for a machine, this ability is currently impossible. Beginning in September 2016 in Cambridge, the BMC3 collaboration will bring together leading brain, cognitive, and computer scientists to conduct research in the field of unsupervised machine understanding of audio-visual streams of data, using insights from next-generation models of the brain to inform advances in machine vision.

Billions of dollars are being poured into building sophisticated artificial intelligence algorithms. But they could all be snatched away if even a tiny door is left open. Researchers have shown that given access to only an API, a way to remotely use software without having it on your computer, it's possible to reverse-engineer machine learning algorithms with up to 99% accuracy. In the real world, this would mean being able to steal AI products from companies like Microsoft and IBM, and use them for free. Small companies built around a single machine learning API could lose any competitive advantage.

We eat, breathe and sleep artificial intelligence; it truly is running the world as we know it! Now, it's the turn of education; an industry that has been somewhat left behind in recent years. This however, is set to change. We've all read countless articles on how artificial intelligence is stealing our jobs, so surely something must be done to counteract this? Artificial intelligence and automation are now beginning to enter the workplace at a more graduate level and so it's about time that universities rethink what they're doing and how they're doing it; that is to equip graduates with the ability to work effectively alongside artificial intelligence.

In the first post of this series, I've written my thoughts about the rise of Artificial Intelligence, and how it will serve as an enabler for ever greater progress. In this one, I deal with its (hopefully) short-term implications. As we've seen, despite all current impressive technological advancements, jobs and professions that call for increased inventiveness and creativity, will remain under human control in the foreseeable future. Even more so for jobs where emotions and empathy play a great role. The same cannot be said for the kind of job, whether manual or mental, that requires little or no creativity.

ReplyBuy, a finalist in the 1st and Future competition, wants to use the text message to get you tickets for sporting events. The current version of ReplyBuy works like this -- the company sends a text message to all San Francisco 49ers fans; whoever replies "Buy Now" the fastest gets the tickets. Today, the company is making the platform immensely more useful with the launch of ReplyBuy.ai. Indeed, ReplyBuy is introducing artificial intelligence to the sports and entertainment vertical. Instead of just receiving text messages when tickets are available, users will now be able to send a text message with a request to buy tickets for whichever event they want; the chatbot will ask a few follow-up questions, like "how many tickets do you want?" and "what's your price range."

Four years ago an article was written for the Harvard Business Review by both Tom Davenport and DJ Patil entitled "Data Scientist: The Sexiest Job of the 21st century." The article predicted that the demand for skilled people in this area was only set to rise and as a recruitment/staffing specialist within this space, I can only confirm that this has come to pass. With this evolution, I now see the waters have become even harder to navigate, with companies now looking to hire people proficient in an even greater number of disciplines. That is why I wanted to do this post. As with any shortage, you need to get creative but from my perspective, I am often a little surprised at how little flexibility there is when looking to employ people for specialist niche roles.

Five years ago, music personalization at Spotify was a tiny team. The team read papers, developed models, wrote data pipelines and built services. Today personalization involves multiple teams in New York, Boston & Stockholm producing datasets, feature engineering and serving up products to users. Features like Discover Weekly and Release Radar are but the tip of a huge personalization iceberg. One thing we have noticed is the overhead of running services.

In the 2016 Google Founder's Letter, CEO Sundar Pichai cited Google's long-term investment in machine learning and AI. "It's what allows you to use your voice to search for information," he explained, "to translate the web from one language to another, to filter the spam from your inbox, to search for'hugs' in your photos and actually pull up pictures of people hugging ... to solve many of the problems we encounter in daily life. It's what has allowed us to build products that get better over time, making them increasingly useful and helpful." In addition to using machine learning for its own products, Google has released several applied machine learning services -- for vision, speech, natural language, and translation -- and has open-sourced its TensorFlow scalable machine learning package. An additional service based on TensorFlow, the Cloud Machine Learning Platform, is still in a closed alpha test phase.