In this episode of The Kennedy-Mighell Report, hosts Dennis Kennedy and Tom Mighell consider the increased popularity of artificial intelligence, the usefulness of chatbots, and how both innovations can impact the practice of law. Tom speculates that the current data age and the large volumes of information available for analysis have helped to enable the advancements in machine learning and artificial intelligence. Dennis explores exactly what machine learning means and explains the three current learning types: unsupervised, supervised, and reinforced. Tom finds the technology perplexing and uses the definition of Tenser Flow to illustrate how grasping these advanced concepts requires more education and technology knowledge than the average lawyer possesses. They both discuss the AI lawyer Ross and if legal professionals should gain technical knowledge in order to influence future ethical regulations with emergent technology.

Stellaris 1.2.5 hotfix patch released, with AI fixes Calling all coders: is AI coming for your job?

Stellaris 1.2.5 hotfix patch released, with AI fixes Calling all coders: is AI coming for your job?

It takes years, decades even, for physicians to refine the expertise required to notice details that remain invisible to the untrained. This aptitude, depending on a doctor's specialty, might mark the difference between an oncologist knowing a malignant tumor from a benign cyst. It can help a cardiologist determine the velocity of blood as it flows through a hole in the heart. Or it may tell a reconstructive plastic surgeon whether a severe burn is healing nicely or at risk of infection. None of this is easy unless you know how to see in a certain way.

While Artificial Intelligence is conquering the chatbot world, something similar is happening in the Marketing Automation industry too. Innovative, new technologies are appearing day by day that reinvents the way we – marketers – do our daily routine. Machine Learning and Artificial Intelligence fundamentally change our job description: solves different pain points we and our company face and help us move to a more strategic position. We need to know what type of skills we have to learn before it is too late. Therefore I asked 14 seasoned experts about their opinion: how could Artificial Intelligence be used in Marketing Automation?

Nasa launches million dollar contest to create'Valkyrie' AI robot that will accompany astronauts on ... Stellaris 1.2.5 hotfix patch released, with AI fixes

In a globally connected marketplace, new technologies ensure customer transactions won't get lost in translation. The world is becoming increasingly connected, and companies in search of worldwide markets need to be able to communicate with customers in their native tongues. They're depending on sophisticated new machine translation technologies to break down language barriers. "When you first enter a market, the early adopters for any new product -- whether it's a personal care product or a tech product -- tend to be internationally focused and English friendly, so you might think you're doing well," said Ben Sargent, content globalization strategist at the consulting firm Common Sense Advisory. To reach 80 percent of the world's total online population, businesses need to communicate in at least 12 languages, and to reach 98 percent, they need to translate across 48 languages.

Adrian is an industry analyst and recovering academic, providing research and advisory services for buyers, sellers, and investors in emerging technology markets. His coverage areas include cognitive computing, big data / analytics, the Internet of things, and cloud computing. Adrian co-authored Cognitive Computing and Big Data Analytics (Wiley, 2015) and is currently writing a book on the business and societal impact of these emerging technologies. He has held executive positions at several consulting and analyst firms. Adrian also held academic appointments in computer science at Drexel University and SUNY-Bingamton, and adjunct faculty positions in the business schools at NYU and Boston College.

I still remember when I trained my first recurrent network for Image Captioning. Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense. Sometimes the ratio of how simple your model is to the quality of the results you get out of it blows past your expectations, and this was one of those times. What made this result so shocking at the time was that the common wisdom was that RNNs were supposed to be difficult to train (with more experience I've in fact reached the opposite conclusion). Fast forward about a year: I'm training RNNs all the time and I've witnessed their power and robustness many times, and yet their magical outputs still find ways of amusing me. This post is about sharing some of that magic with you. We'll train RNNs to generate text character by character and ponder the question "how is that even possible?" By the way, together with this post I am also releasing code on Github that allows you to train character-level language models based on multi-layer LSTMs. You give it a large chunk of text and it will learn to generate text like it one character at a time. You can also use it to reproduce my experiments below.

AI has impacted our day-to-day lives for years, whether that's automated voice calls or virtual personal assistants - like Siri - or even self-driving cars. The next step is to implement AI technology into personal and cyber security systems. Currently, one or two guards will monitor a bank of security screens, and it is a successful method of security, but it is not full proof. Eliminating human error is a key driver behind bringing Artificial Intelligence to security through intelligent video analytics. Humans can easily get distracted, generally have short attention spans, and often find it difficult to focus on multiple things at once - a bank of security screens.