The new machine-learning research center will focus on turning Google's ambitions in this space to reality, the company says.

'Til your good is better and your better is best." H2O now has random hyperparameter search with time- and metric-based early stopping. Bergstra and Bengio[1] write on p. 281: Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Even smarter means of searching the hyperparameter space are in the pipeline, but for most use cases random search does as well. Nearly all model algorithms used in machine learning have a set of tuning "knobs" which affect how the learning algorithm fits the model to the data.

Intelligent, often autonomous, systems are making headway in the datacenter as developers seek to offload manual processes and move beyond traditional approaches like prescriptive IT automation as they struggle to keep up. That's the conclusion of a vendor-backed survey of IT executives about the adoption of intelligent machines and systems based on new AI approaches and other expert systems. Still, the survey sponsored by IT management software specialist Ipswitch notes that early adopters of automated systems are struggling to gauge security and access risks associated with handing the keys to bots and other electronic assistants. "IT decision makers recognize that, while a force for good, these technologies also expose the enterprise to new internal and external risk vectors," Tony Lock, an analyst with survey author Freeform Dynamics, noted in a statement. "As the pace of adoption increases, there will be no escaping the impact of intelligent systems on the enterprise, regardless of whether or not organizations directly invest in such technologies."

Honda Motor Co. will open a new artificial intelligence (AI) research center in Tokyo to commercialize self-driving cars in the future. Honda R&D Co., Honda's research and development subsidiary, announced on June 2 that it will establish Honda R&D Innovation Lab Tokyo by around September in Tokyo's Akasaka district. The move came as Japanese auto companies are desperately seeking personnel who specialize in the cutting-edge area. AI is viewed as the core technology to develop futuristic driverless vehicles. Honda already has a research center in the northern Kanto region, but it will set up a new facility in the capital to hire more skilled information technology engineers.

Artificial intelligence is a common topic explored within the science-fiction genre. Sunspring, a new sci-fi short, instead of using the theme of artificial intelligence in its narrative, used AI to actually produce the narrative in the first place. The film, which had its online debut on Ars, had its screenplay written by an AI which goes by the name of'Benjamin'. The film was submitted as part of the 48-Hour Film Challenge at the Sci-Fi London film festival by Oscar Sharp, a BAFTA-nominated filmmaker and Ross Goodwin, a creative technologist and former Obama administration ghostwriter. Sunspring comes to life through the acting and production. The screenplay is sufficiently vague and nonsensical to leave a lot of room for interpretation.

SugarCRM held its annual user conference, SugarCon, earlier this week in San Francisco. Over 1,200 people were on hand to see what direction the company was taking its platform in. And while many of its competitors are expanding their offerings to include ecommerce, configure-price-quoting (CPQ), marketing automation and other related areas, SugarCRM CEO Larry Augustin emphatically stated that Sugar is staying true to its CRM roots and explore the many ways CRM can be even more important to business success in the near future. I had a chance to speak with Augustin and hear how the company is looking to bring the latest and greatest technology developments to the platform to make CRM a system more relevant, and attractive, for sales professionals to turn to in order to build successful relationships with modern consumers. Below is an edited transcript of that conversation, as well as the video of our full conversation.

At lunch last week, I learned that a couple colleagues were engaged in a little duel--trying to out-walk each other, as tracked by their new Fitbits. Self-improvement was definitely the goal, but seeing peer performance and benchmarks provided the required motivation to achieve that goal. If you're a data science or analytics leader, your job is to manage analysts who produce insights. So, if you want to drive your business through these insights, you have two options. You can either hire more analysts or you can increase the productivity of your existing analysts.

I come from the Cambridge machine learning group. More than once I heard people referring to us as "the most Bayesian machine learning group in the world". I mean, we do work with probabilistic models and uncertainty on a daily basis. Maybe that's why it felt so weird playing with those deep learning models (I know, joining the party very late). You see, I spent the last several years working mostly with Gaussian processes, modelling probability distributions over functions. I'm used to uncertainty bounds for decision making, in a similar way many biologists rely on model uncertainty to analyse their data. Working with point estimates alone felt weird to me. I couldn't tell whether the new model I was playing with was making sensible predictions or just guessing at random. I'm certain you've come across this problem yourself, either analysing data or solving some tasks, where you wished you could tell whether your model is certain about its output, asking yourself "maybe I need to use more diverse data? or perhaps change the model?". Most deep learning tools operate in a very different setting to the probabilistic models which possess this invaluable uncertainty information, as one would believe. I recently spent some time trying to understand why these deep learning models work so well – trying to relate them to new research from the last couple of years. I was quite surprised to see how close these were to my beloved Gaussian processes. I was even more surprised to see that we can get uncertainty information from these deep learning models for free – without changing a thing. Update (29/09/2015): I spotted a typo in the calculation of \tau; this has been fixed below.

In this work, we particularly focus on the complex relationship between land-use and transport offering an innovative approach to the problem by using land-use features at two differing levels of granularity (the more general land-use sector types and the more granular amenity structures) to evaluate their impact on public transit ridership in both time and space. To quantify the interdependencies, we explored three machine learning models and demonstrate that the decision tree model performs best in terms of overall performance--good predictive accuracy, generality, computational efficiency, and "interpretability". We then demonstrate how the developed framework can be applied to urban planning for transit-oriented development by exploring practicable scenarios based on Singapore's urban plan toward 2030, which includes the development of "regional centers" (RCs) across the city-state. This trend, on the other hand, eventually reverses (particularly during peak hours) with continued strategic increase in amenities; a tipping point at 55% increase is identified where ridership begins to decline and at 110%, the predicted ridership begins to fall below current levels.

I was initially writing on a different topic (related to analytics). I had almost finished writing it. I had put in about 2 hours and written an average article. If I had made it live, it would have done OK! But something in me stopped me from making it live.