Artificial intelligence (AI) is no longer a futuristic concept, it's a staple of today. From virtual personal assistants like Siri and Cortana, to image scanners built to identify diseases, to Google's or Tesla's self-driving cars, AI is becoming a part of everyday technology. According to a MarketsandMarkets report, the artificial intelligence industry is estimated to reach USD 5.05bn by 2020, growing at an annual growth rate (CAGR) of 53.65% between 2015 and 2020. One reason for this significant growth is the increased use of machine learning technology – a subcategory of AI where computers learn from data themselves in the advertising and media industry. Machine Learning has a huge impact on the advertising ecosystem already.

We already know how artificial intelligence has been affecting marketing, with everyone from Netflix to Under Armor utilizing data to improve customer experience, but how exactly can market researchers hope to use AI? And apart from all those hip and trendy businesses using it that we hear about – are there really any implications for non-tech related companies?

Christoffer O. Hernæs is vice president of Strategy, Innovation and Analysis at Sparebank 1 Group, Norway's second-largest financial institution. When I was growing up, my father often told me that Andy Warhol once said that he wanted to be a machine, and that it would be a lot easier to be a machine -- if something broke, you could just replace it. Even though small wounds and injuries heal, this has not been the case for humans. If something were inherently broken, it would stay broken the rest of our lives. Which relates to another common saying: The only two things in this world that are certain are death and taxes.

Microsoft is working towards fighting cancer using computer science such as machine learning and algorithms. By treating cancer like an information processing system, Microsoft researchers are able to adapt tools typically used to model computational processes to model biological ones. Ultimately, the company hopes to create molecular computers to program the body to fight cancer cells immediately after detection. "We are trying to change the way research is done on a daily basis in biology," said Jasmin Fisher, a senior researcher who works in the programming principles and tools group in the Microsoft's research lab in Cambridge. This is combined with a data-driven approach; putting machine learning at the core of Microsoft's attempts to try to tackle the disease.

In April several news sources reported that the law firm of Baker Hostetler had hired a legal robot called ROSS. This artificial intelligence system, built on IBM's famous Watson system, automates legal tasks like research and the preparation of legal memorandums in the narrow domain of bankruptcy law. Since then, other big law firms have joined the rush. ROSS Intelligence markets its product as a kind of artificial intelligent assistant: ask it questions in plain language, and legal research is presented. It sounds a lot like Siri, except instead of asking for baseball scores one can ask for relevant case law.

Just weeks ago 20th Century Fox released a trailer for the upcoming horror film "Morgan," and frankly it's not the best trailer. So then why is it worth talking about? The reason it has made such an impact on our industry is that the trailer was edited partially with the assistance of artificial intelligence, namely the IBM system'Watson.' What does this mean for our future in this industry? And more importantly, what does this mean for your job specifically?

Will chatbots supersede apps and websites? A chatbot is a service powered by rules and sometimes artificial intelligence (AI), which you interact with via a chat interface. An example: If you need new shoes, instead of going to the store and talking to the salesperson about what you want or scrolling through all shoes on a website, you would simply tell the chatbot what you want: your size and your preferred style. Then you would get returned the results as if you were in the store. Chatbots are supposedly huge because for the first time in history, messaging apps have surpassed social networks with over three billion active users per month (almost ½ of the world's population).

One of these days, the walls may know when you're happy, sad, stressed or angry by using an experimental device unveiled Tuesday by researchers at the Massachusetts Institute of Technology that uses wireless signals to recognize emotions through subtle changes in breathing and heartbeat. Computer scientist Dina Katabi and her colleagues at the university's Computer Science and Artificial Intelligence Lab developed a radar system for vital signs that uses reflected radio signals to track movements, moods and behavior, with potential applications for smart homes, offices and hospitals. They posted their new research online Tuesday and plan to present their test results next month at a mobile-computing conference in New York. These wireless signals--a thousand times less powerful than conventional Wi-Fi--are designed to bounce off anyone within range, capturing variations in vital signs that can be analyzed quickly by a computer algorithm able to detect emotional states, the researchers said. To distinguish one mood from another, their system measures patterns of respiration, cardiac rhythms, and minute variations in the length of each individual heartbeat.

Artificial Intelligence is the latest hotness. We've heard how bots are going to be the new apps allowing natural conversational interfaces. Siri, Cortana and Google Now are no longer a gimmick and are becoming more useful every day. Machine Learning sits behind artificial intelligence and on the surface it may seem like a deeply technical, scientific topic. We are here to tell you that it is more accessible than you think.

You just have to submit the description of your problem, some pointers as to how to get started, and provide lightweight supervision along the way (occasionally answer questions, provide feedback, suggest experiments to try...). What you get out of this: - Innovative solutions to research problems that matter to you. - Full credits for the value you provide along the research process. We are looking for both deep learning research problems, and problems from other fields that could be solved using deep learning. Note that the information you submit here may be made public (except for your contact information). We will create a website listing the problems submitted, where people will be able to self-organize into teams dedicated to specific problems.