In the high tech world, AI has recently gained lots of traction as it seems to be a crucial way to deliver business value out of the big data as well described in Is Big Data Still a Thing? But recently, a day after Microsoft introduced an innocent AI chat robot to Twitter it had to be deleted after it transformed itself into a very evil one. It shows that AI needs to be well trained otherwise it can easily go out of track. When doing business, this is the last thing you want to happen: it's better to know where you're going. Our customers or prospects often have questions such as "what's the difference between Machine Learning and Deep Learning?",

Hyperparameter optimization is a common problem in machine learning. Machine learning algorithms, from logistic regression to neural nets, depend on well tuned hyperparameters to reach maximum effectiveness. Different hyperparameter optimization strategies have varied performance and cost (in time, money, and compute cycles.) So how do you choose? Evaluating optimization strategies is non-intuitive.

Let's suppose you've agreed to a rather odd travel program, where you're going to be suddenly transported to a randomly selected country, and your job is to figure out where you end up. So, here you are in some new country, and all countries are equally likely. You make a list of places and probabilities that you're in those places (all equally likely at about 1/200 for 200 countries). You look around and appear to be in a restaurant. Some countries have more restaurants (per capita/per land area) than others, so you decrease the odds that you're in Algeria or Sudan and increase the odds that you're in Singapore or other high-restaurant-density places. That is, you just multiply the probability that you were in a country with the probability of finding oneself in a restaurant in that country, given that one were already in the country, to obtain the new probability. After a few moments, the waitress brings you sushi, so you decrease the odds for Tajikistan and Paraguay and correspondingly increase the odds on Japan, Taiwan, and such places where sushi restaurants are relatively common. You pick up the chopsticks and try the sushi, discovering that it's excellent. Japan is now by far the most likely place, and though it's still possible that you're in the United States, it's not nearly as likely (sadly for the US). Those "probabilities" are getting really hard to read with all those zeros in front. All that matters is the relatively likelihood, so perhaps you scale that last column by the sum of the whole column. Now it's a probability again, and it looks something like this: Now that you're pretty sure it's Japan, you make a new list of places inside Japan to see if you can continue to narrow it down. You write out Fukuoka, Osaka, Nagoya, Hamamatsu, Tokyo, Sendai, Sapporo, etc., all equally likely (and maybe keep Taiwan too, just in case). Now the waitress brings unagi. You can get unagi anywhere, but it's much more common in Hamamatsu, so you increase the odds on Hamamatsu and slightly decrease the odds everywhere else. By continuing in this manner, you may eventually be able to find that you're eating at a delicious restaurant in Hamamatsu Station -- a rather lucky random draw.

This final state, which is stored in state is then used as the initial state in this line feed {self.input_data: The state variable has the real state information. When you construct the feed dictionary with self.initial_state:state, it basically means use state (the actual state value) as self.initial_state

Your next trip could be planned by... artificial intelligence? A new startup called Lola aims to revolutionize the concept of travel agents by making trip booking as easy as texting a friend. The company, which was started by Kayak founder Paul English, officially launches for iPhones Thursday, with a small team of travel bookers who will rely on super-powered artificial intelligence algorithms that will help them manage as many as hundreds of clients at once. Here's how it works: After setting up an account--and punching in credit card details--Lola users can simply message a representative through the app, whether they need a simple round trip to Chicago or want to plan a weeklong hiking expedition through Indonesia. While a traditional booking site would make you type in dates, preferences, and countless other parameters, Lola promises to simplify the process by letting users make requests in conversational language: You might type "need a flight to O'Hare next weekend" instead of punching info into little boxes.

Acclaimed physicist, author and educator Stephen Hawking has claimed that artificial intelligence may cause the end of civilization as we know it. While I don't think we're headed for a dire, Terminator-like state, there's little doubt that artificial intelligence has the capacity to change the world. That change first affected industrial and mechanical industries by assisting in mass production, but today businesses of all types are discovering the benefits of artificial intelligence in the workplace. Leading organizations are employing artificial intelligence to work alongside employees for more effective and efficient results. Artificial intelligence capabilities can be grouped into three main categories: cognitive computing, machine learning and deep learning.

You probably didn't consider basing your Kentucky Derby bets on artificial intelligence -- but maybe you should have. The artificial intelligence company Unanimous tested its new software platform, UNU, on last weekend's Kentucky Derby, as reported by TechRepublic. Twenty participants, convened by the company, first used the software to narrow the field of 20 horses down to four top picks. The participants then used UNU to predict the winning order -- and it turned out to be 100 percent correct. "I placed my 1 bet on the race at the Derby on Saturday and made 542.10 -- the odds of winning the superfecta [the top 4 finishers in order] were 540-1," TechRepublic reporter Hope Reese wrote.

Many travel agencies are working to better integrate mobile messaging into the travel booking process for clients. But a new service from online travel veterans is looking to take a technology-first approach to providing concierge-like service in the online travel booking space. Lola launches today on the Apple App Store, combining augmented chat with artificial intelligence and a staff of travel agents and customer service specialists to book travel for users. An Android-compatible version will follow before the end of the year. Kayak co-founder Paul English's newest project pairs deep-learning artificial intelligence technology with travel agents and customer service experts in order to present users with a more organic travel booking experience than has previously been available to both leisure and business travelers.

The first job after law school can be horrendous--not simply because of the intense workload and long hours, but also the drudgery. A huge amount of legal work given to those on the lowest rung of the ladder consists of reading through hundreds of pages of notes, articles, and case precedents, to provide senior lawyers with legal details that can help build their case. Fortunately, artificial intelligence is up to the task. So much so that century-old law firm BakerHostetler has formally hired its first "digital attorney," ROSS, as an artificially intelligent legal researcher. ROSS is working with BakerHostetler's bankruptcy team as part of a partnership first announced last month, at Vanderbilt Law School's "Watson, Esq." conference on law and artificial intelligence.

"i want to talk to you." "i want to be with you." "i don't want to be with you." i don't want to be with you. What reads like bad teen poetry is actually supposed to help Google sound more human. Researchers from the company's deep learning arm, Google Brain, is hoping to find ways to make its search and apps understand and adapt to the way people actually speak--in part by feeding 2,865 romance novels to an AI system. The unpublished paper was presented at the International Conference on Learning Representations on May 3. It shows how the team of linguists and computer scientists poured 11,000 yet unpublished books--including nearly 3,000 romance and 1,500 fantasy novels--into a neural network model, which is meant to mimic how the human brain works. Next, the researchers presented the system with two sentences from the books and asked it to generate sentences that could create a meaningful progression between the two.