It won't help you find Pikachu, but a new mobile tool from Macy's will assist shoppers in navigating the store. The department store retailer is piloting "Macy's On Call," a solution that lets in-store customers use their mobile phones to interact with an artificial intelligence (AI) platform powered by IBM Watson. The Satisfi intelligent engagement platform provides the connection between the mobile tool and the Watson platform. Macy's On Call, accessed via a mobile browser, allows customers to input natural language questions about the store's unique product assortment, services and facilities. These can include store navigation details, as well offerings such as the myStylist personal shopper program and buy-online-pickup-in-store counters.

Artificial intelligence is certainly affecting everything we do. And much of that innovation with AI comes from venture funding. So it's no surprise that one day, someone would think of mixing these two things. It, in its own words, is "The world's first Venture Capital fund powered entirely by Artificial Intelligence." According to the site, The AIVC is 100% data driven. It uses historical data and advanced predictive algorithms to make investment decisions, much like a normal VC would.

Artificial intelligence has replaced big data this year as the most talked about new set of technologies. As with big data five years ago--behind the hype, the confusion generated by an ill-defined term, and the record funding by VC--we are starting to see emerging investments and practical applications where it matters most--in enterprises. A new report from Narrative Science, based on a survey of 235 business executives conducted by the National Business Research Institute (NBRI), sheds light on the state-of-AI in enterprises today and in the future: 38% of enterprises are already using AI technologies and 62% will use AI technologies by 2018. Keep in mind that "AI technologies" is a broad term that includes machine and deep learning, recommendation engines, predictive and prescriptive analytics, automated written reporting and communications, and voice recognition and response. Big data has spawned the current interest and increased investment in artificial intelligence.

First was the Monte Carlo tree search, an algorithm that rather than attempting to examine all possible future moves instead tests a sparse selection of them, combining their value in a sophisticated way to get a better estimate of a move's quality. The second was the (re)discovery of deep networks, a contemporary incarnation of neural networks that had been experimented with since the 1960s, but which was now cheaper, more powerful, and equipped with huge amounts of data with which to train the learning algorithms. The combination of these techniques saw a drastic improvement in Go-playing programs, and ultimately Google DeepMind's AlphaGo program beat Go world champion Lee Sedol in March 2016. Now that Go has fallen, where do we go from here? Following Kasparov's defeat in 1997, scientists considered that the challenge for AI was not to conquer some cerebral game.

Artificial intelligence platform startup that seeks to automate the notification and messaging process to personalize consumer experience is seeking a Machine Learning Engineer. The Machine Learning Engineer will develop the recommendation models and systems behind this AI platform. Benefits include competitive compensation package, equity, bonus, full benefits, paid time-off, and a great team work environment with company outings and collaborative work culture. This startup has received close to 20M in funding, and this is a ground floor opportunity to apply industry data science experience and develop something from scratch. You will be able to see your impact on the core product!

Ever wanted a way to easily extract and analyze meaningful data from text? The new Cloud Natural Language API has a feature that lets you extract entities from text -- like people, places and events -- with a single API call. Let's take the following sentence from a recent news article: LONDON -- J. K. Rowling always said that the seventh Harry Potter book, "Harry Potter and the Deathly Hallows," would be the last in the series, and so far she has kept to her word. I could write my own algorithm to find the people and locations mentioned in this sentence, but that would be difficult. And it would be even more difficult if I wanted to gather more data on each of the mentioned entities, or analyze entities across thousands of sentences, while accounting for mentions of the same entity that are phrased differently (e.g., "Rowling" and "J.K. Rowling"). With Cloud Natural Language API, I can analyze the above sentence with the API's analyzeEntities method.

From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world's most challenging physical problems -- such as energy consumption. Large-scale commercial and industrial systems like data centers consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world's increasing need for computing power. Google is taking many steps to reduce energy consumptions . Compared to five years ago, Google now get around 3.5 times the computing power out of the same amount of energy.

It's fair to say that Google-owned AI company DeepMind's big push into the health space via data-access collaborations with the UK's National Health Service -- announced with much fanfare in February this year -- has not been running entirely smoothly so far. But there are more regulatory bumps in the road ahead for DeepMind Health. TechCrunch has learned the company won't continue using one of the apps it co-designed with the NHS until the software has been registered as a medical device with the relevant regulatory body, the MHRA. That's especially interesting given that this app, called Streams, has already been used for patient care in multiple NHS hospitals. The Royal Free NHS Trust previously told TechCrunch the app had been used by up to six of its clinicians in three "user tests" in its London hospitals. Which, put another way, means a profit-driven commercial entity has been involved in a real-world test of an unregistered medical device on actual hospital patients.

Google is putting DeepMind's machine learning to work on managing their sprawling data centers' energy usage, and it's is performing like a boss -- the company reports a 15% drop in consumption since the AI took over. Google is undeniably a huge part of western civilization. The company's data servers pretty much handle all of my mail at this point, along with YouTube, social media platforms and much more. But even so, it's easy to forget that the Google we know and interact with every day is just the tip of the iceberg; it relies on huge data servers to process, transfer and store information -- and all this hardware needs a lot of power. So much power, in fact, that the company decided to do something about it.

In what the top-tier Danish law firm says is its first step to full implementation of an artificial intelligence solution, Bech-Bruun has selected both RAVN Connect Enterprise and RAVN Manage for advanced enterprise search and knowledge management. RAVN Connect Enterprise is RAVN System's next gen enterprise search solution, released in April 2014, which enables firms to find, manage and collaborate on their knowledge and expertise. Features include tagging, linking, voting and grading as well as a knowledge graph that highlights the relationship between knowledge and people. RAVN Manage monitors the health of its enterprise applications and exposes any hidden problems. Bech-Bruun, which with over 500 staff is one of the largest in the Nordic region, is using the RAVN Connect Enterprise search platform to search a wide variety of internal content including iManage Work, InterAction CRM, Navision and Mimecast.