If you need a scalable recommendation library you probably look at MlLib from Spark. Is it always a good choice? Is it the best solution for You? In this presentation I help you to understand what are strong and week points of MlLib and when MlLib is not for you. It could happens (and probably will), that MlLib is not for you.

There is a plethora of new technologies entering the big data landscape, but perhaps the most avidly discussed in 2015 was Apache Spark. Some view this tool as a more accessible and powerful alternative to Hadoop, while others argue Spark can be used as a powerful complement to Hadoop, with its particular strengths and quirks. But what are the facts? Who is using Spark and how does it differ from other data processing engines? An all-purpose data processing engine, Spark can be used for a variety of operations. Data scientists and application developers can integrate Spark into their applications to query, analyse, and transform data quickly and at scale.

While being a vibrant subfield of computer science, machine learning is used for drawing models and methods from statistics, algorithms, computational complexity, control theory and artificial intelligence. It focuses on efficient algorithms for inferring good predictive models from large data sets and is natural candidate for problems arising in HFT – both trade execution & alpha generation. In quantitative finance inference of models of predictive nature using historical data is obviously not new. Some examples include the coefficient estimation for CAPM, Fama and French factors. The granularity of data arising in HFT poses special challenges for machine learning. Often data microstructure at the resolution of individual orders, executions, hidden liquidity and cancellation including lack of understanding of how such granular data relates to actionable circumstances, namely profitably buying or selling shares, optimally executing a large order, etc.

With a photo of Zuckerberg and his bodyguards jogging past Tian'anmen Square in Beijing on a heavily polluted day Friday, Zuckerberg announced his arrival and sparked heated online discussion in the process. He continued to grab public attention as he met and had a dialogue with Chinese tech mogul Jack Ma, the chairman of the Chinese tech mammoth Alibaba Group, at the China Development Forum, a state-sponsored forum. What did the two discuss? Have a look with AllChinaTech. On innovation When asked about his opinion of China's next five-year development plan, which highlights innovation, Zuckerberg talked about innovation being dedicated to solving long-term problems.

While the big gurus of futurism are talking about artificial intelligence as a threat to humanity, with this post I would like to focus your attention on a short term amazing application of it. Your next personal assistant will be virtual, cheap and live on your walls. There are three major trends colliding: the first is the development of artificial intelligence, the second is the promise of OLED screens that can be folded, and the third is the cloud. If you are a fan of AI, you probably know a lot about its recent developments. What I see from my observatory is that more and more AI is moving from a top-down to a bottom-up approach, using neural networks.

This downward trend in new job creation in new technology industries is particularly evident starting in the Computer Revolution of the 1980s. For example, a study by Jeffery Lin suggests that while about 8.2% of the US workforce shifted into new jobs during the 1980s which were associated with new technologies; during the 1990s this figured declined to 4.4%. Estimates by Thor Berger and Carl Benedikt Frey further suggest that less than 0.5% of the US workforce shifted into technology industries that emerged throughout the 2000s, including new industries such as online auctions, video and audio streaming, and web design.

A mechanical wonder from the brainiacs at Stanford University. They have created a team of six tiny robots that can pull a car. Like ants, the uBots work as a team to move objects hundreds of times their size, like a frog being carried along by ants. The tiny robots use micro-adhesion and team-load sharing, to conquer the weight of the nearly 4,000-pound car. The Stanford researchers noticed ants can boost their lifting and dragging power by using three-out-of-their-six legs at once.

Dentons turns to platform-as-a-service technology, analytics and artificial intelligence to build a better framework for legal research and processes. Although the legal profession has advanced considerably through the use of digital technology, many tasks remain deeply rooted in the past. In most cases, firms use an array of ad hoc and disconnected tools to do their job. It's not surprising, then, that the introduction of artificial intelligence and analytics could revolutionize the field. "Firms are searching for ways to build a better framework for legal research and processes so that they can operate faster and keep costs down," says Joe Andrew, the global chairman of Dentons, which operates more than 125 offices in over 50 countries.

The only way to keep up with this IoT-generated data and gain the hidden insights it holds is using AI (Artificial Intelligence) as the last mile of IoT. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines" In an IoT situation, AI can help companies take the billions of data points they have and boil them down to what's really meaningful. The general premise is the same as in the retail applications – review and analyze the data you've collected to find patterns or similarities that can be learned from, so that better decisions can be made. The data collected, combined with AI, makes life easier with intelligent automation, predictive analytics and proactive intervention.

The possibilities that IoT brings to the table are endless. IoT continues its run as one of the most popular technology buzzwords of the year, and now the new phase of IoT is pushing everyone to ask hard questions about the data collected by all devices and sensors of IoT. IoT will produce a tsunami of big data, with the rapid expansion of devices and sensors connected to the Internet of Things continues, the sheer volume of data being created by them will increase to an astronomical level. This data will hold extremely valuable insights into what's working well or what's not. Also, IoT will point out conflicts that arise and provide high-value insight into new business risks and opportunities as correlations and associations are made.