Israeli startup Revuze, which provides Artificial Intelligence (AI) to both brand and product management, has closed a 4 million seed-funding round led by strategic investors Nielsen, The NPD Group, and TIC Group. Revuze is also entering into business development partnerships to introduce its transformative AI-led technology to its investors' customers. Headquartered in Netanya, Revuze will use this investment, to expand US operations and open offices in San Francisco and New York City. Revuze uses AI, powered by neural networks and machine learning, to empower the brand and product management industries that previously have relied on manually intensive solutions, such as text analytics, social listening and monitoring. These current solutions, requiring months to execute, demand teams of product experts, data scientists and analysts to construct and maintain rules, dictionaries and taxonomies before interpreting the findings.

Mumbai: Typing a query in an online search box is straightforward for users. It's not so for search engines that have to crawl trillions of pages, track links on them, sort them by content, then index the pages and also have their algorithms understand what the queries mean before dishing out the answers--all in less than a second. More so, for a company like Google, which processes billions of searches daily--making search "core" to the company's mission of organizing "the world's information" and making it "universally accessible and useful". When Google was founded in September 1998, it was serving around 10,000 search queries per day. The company now processes more than 40,000 every second on average, which translates to over 3.5 billion searches per day and 1.2 trillion per year worldwide, according to internetstatslive.com.

Microsoft has created a new artificial intelligence unit, as the company pushes deeper into the fast-growing field. Almost whole of Silicon Valley is diving into artificial intelligence (AI)and machine learning research, an industry estimated to boom to 70 billion by 2020 from just 8.2 billion in 2013, according to a Bank of America report that cited IDC research. On Wednesday, Microsoft teamed up with four other big technology companies - Amazon Inc, Google, Facebook and IBM - to create a non-profit organization to advance public understanding of AI technologies. The new unit called Microsoft AI and Research Group, will be headed by Harry Shum, a company veteran who has held senior roles at the Microsoft Research and Bing engineering divisions. "Microsoft has been working in artificial intelligence since the beginning of Microsoft Research, and yet we've only begun to scratch the surface of what's possible," Shum said in a statement.

While companies know data is the currency of business, it's still a struggle to make that data insightful and actionable. Companies are using technologies such as artificial intelligence (AI) to augment their internal data through intelligent systems in order to learn more about customers. At the same time, bringing intelligence to bear on internal data systems doesn't mean companies know what to do with that intelligence. According to Bluewolf Group's most recent "State of Salesforce" report, just more than half of the 1,700-plus respondents (52%) said they have intelligent systems, while 80% of the data that undergirds these applications remains untouched. Companies have little visibility into the nature or meaning of that data because it is unstructured and more difficult to analyze than traditional, numeral-based data.

Labeled the "Microsoft AI and Research Group" and to be led by 20-year Microsoft (NASDAQ:MSFT) veteran, Harry Shum. Over 5K computer scientists and engineers will be involved under the new structure. Shum's existing team along with Information Platform, Cortana and Bing, and Ambient Computing and Robotics teams to also integrate. CEO Satya Nadella: "We live in a time when digital technology is transforming our lives, businesses and the world, but also generating an exponential growth in data and information. At Microsoft, we are focused on empowering both people and organizations, by democratizing access to intelligence to help solve our most pressing challenges. To do this, we are infusing AI into everything we deliver across our computing platforms and experiences."

In the last post, we talked about how the open sourcing of machine learning algorithms and hardware architecture gives rise to the latest phenomenon of "Data is king". As companies compete to get developers to use their libraries, we continue to see this ongoing arms race like trend in open sourcing. In addition, we are also seeing how complicated the big data landscape is becoming. This is the link to the most recent 2016 Big Data Landscape compiled by Matt Turck. There are several new entrants into niche areas in the landscape.

It used to be that computer vision - getting a computer to not only'see', but also to'understand', the objects around it - was one of the hardest problem for engineers and researchers to solve. However, with modern advances in neural networks and machine learning, computer vision has become common place. Now, Google has shared the latest big advancements in this space, which spell doom for humanity. A cross between computer vision and machine learning, the system analyzes images and then describes what it sees in captions, which are supposed to be easily understandable and as human-like as possible. We've seen previous incarnation of such systems from both Google and Microsoft, not to mention other companies in the IT space.

Increased investor interest in AI startups – from around 10 deals in Q1'11 to over 120 in Q2'16 – can be attributed to recent advances in machine learning algorithms, particularly "deep learning" technology, a souped up version of AI. Just this week, Google integrated deep learning into its Google Translate tool; Baidu announced the launch of DeepBench, an "open source benchmarking tool for evaluating deep learning performance across different hardware platforms"; and NVIDIA introduced Xavier, a deep learning-based supercomputer for driverless cars. In the private market, Google put deep learning in the spotlight back in 2014 when it acquired 4 startups focused on this AI tech in quick succession: DeepMind, Vision Factory, Dark Blue Labs, and DNNresearch. Apple, which joined the race in 2015, most recently acquired Turi, which has developed a deep learning toolkit, among other AI-based solutions. Not to be outdone, Intel has acquired more than 5 AI startups this year alone, including deep learning startup Nervana Systems and, more recently, Movidius.

In this video, we look at how simple AI systems work, and how we can create "smarter" AI that create and execute plans. We'll learn how to code a modified form of the GOAP AI in a future programming tutorial. This month's dev challenge theme is artificial intelligence! They are used under fair use.

The ads will use a genetic algorithm to determine which genes in the ads including elements like copy, layout, fonts and image are successful. Elements that fail to trigger an engagement will be killed off where as those that do will be reproduced in future executions.