Google Tutorial on Machine Learning


This presentation was posted by Jason Mayes, senior creative engineer at Google, and was shared by many data scientists on social networks. Chances are that you might have seen it already. Below are a few of the slides. The presentation provides a list of machine learning algorithms and applications, in very simple words. It also explain the differences between AI, ML and DL (deep learning.)

GPU killer: Google reveals just how powerful its TPU2 chip really is


A custom high-speed network in TPU2s means they can be coupled together to become TPU Pod supercomputers. So far, Google has only provided a few images of its second-generation Tensor Processing Unit, or TPU2, since announcing the AI chip in May at Google I/O. The company has now revealed a little more about the processor, the souped-up successor to Google's first custom AI chip. As spotted by the Register, Jeff Dean from the Google Brain team delivered a TPU2 presentation to scientists at last week's Neural Information Processing Systems (NIPS) conference in Long Beach, California. Earlier this year, Dean explained that the first TPU focused on efficiently running machine-learning models for tasks like language translation, AlphaGo Go strategy, and search and image recognition.

RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry


Synopsis Artificial Intelligence is presently experiencing a renaissance in development of new methods and practical applications to ongoing challenges in Chemistry. We are pleased to announce that the Biological & Medicinal Chemistry Sector (BMCS) and Chemical Information & Computer Applications Group (CICAG) of the Royal Society of Chemistry are organising a one-day conference entitled Artificial Intelligence in Chemistry to present the current efforts in applying these new methods. We will combine aspects of artificial intelligence and deep machine learning methods to applications in chemistry. Outline Programme The programme will include a keynote lecture from a key opinion leader in the field, followed by a number of talks and flash poster presentations throughout the day. This meeting will be of interest to scientists of any level of experience from academia and industry.

A Day in the Life of a Data Scientist


A number of weeks ago I solicited feedback from my LinkedIn connections regarding what their typical day in the life of a data scientist consisted of. The response was genuinely overwhelming! Sure, no data scientist role is the same, and that's the reason for the inquiry. So many potential data scientists are interested in knowing what it is that those on the other side keep themselves busy with all day, and so I thought that having a few connections provide their insight might be a useful endeavor. What follows is some of the great feedback I received via email and LinkedIn messages from those who were interested in providing a few paragraphs on their daily professional tasks.



After remaining tight-lipped for years, Apple is now more than eager to share how much progress it's making on self-driving car technology. AI research director Ruslan Salakhutdinov made a presentation this week that revealed more of what the company's autonomous driving team has been up to. Some of the talk was familiar, but there were a few new examples of how far the fledgling project had come. To start, Apple has crafted a system that uses onboard cameras to identify objects even in tricky situations, such as when raindrops cover the lens. It can estimate the position of a pedestrian even if they're hidden by a parked car.

3 ideas to add to your data science toolkit


I'm always on the lookout for ideas that can improve how I tackle data analysis projects. I particularly favor approaches that translate to tools I can use repeatedly. Most of the time, I find these tools on my own--by trial and error--or by consulting other practitioners. I also have an affinity for academics and academic research, and I often tweet about research papers that I come across and am intrigued by. Often, academic research results don't immediately translate to what I do, but I recently came across ideas from several research projects that are worth sharing with a wider audience.

The Rise of the Digital Workplace Chatbot


Chatbots have invaded the workplace. Or at least, invaded the conversations around the digital workplace. In the last month, I have seen some outstanding presentations on the use of conversational interfaces (aka chatbots, aka bots) in the digital workplace/intranet context. While a number of interesting articles have been published on the topic in the last year, the presentations had a hands-on, "lets write a simple bot" element to them that made the abstract real. What follows is an overview of some of these presentations to get you thinking about how bots might benefit your digital workplace.

Senior #Architect #Machine Learning #NLP - San Francisco, CA


Take the technical lead on client engagements from initial presentations through implementations Contribute to the overall strategy and goal development for Machine Learning/ RPA automation product Educate lines of business/services on value afforded by automation and assist with intake process of new use cases Create product roadmap to support goals, sequence of'bots'/ 'builds' and recommend go-live plan Evaluate total automation opportunities across North America Prepare materials for tech/dev, LOB and stakeholder meetings, including but not limited to measurements against KPIs, status updates, and PowerPoint presentations Collaborate with internal partners. You will be exceptional at developing and nurturing relationships with key internal stakeholders in support all of our client's lines of business. Create product roadmap to support goals, sequence of'bots'/ 'builds' and recommend go-live plan You will be exceptional at developing and nurturing relationships with key internal stakeholders in support all of our client's lines of business. Ability to work independently with directional guidance from leadership An ability to meet commitments, build consensus, negotiate resolutions, and garner respect from other teams Experience with agile product management Excellent presentation skills, comfortable presenting to senior leadership Able to sell the value proposition of machine learning/ RPA automation to all Commercial LOBs Strong attention to detail, combined with ability to see and explain the big picture Ability to deal with ambiguity, change, and shifting priorities Ability to manage and prioritize multiple projects simultaneously Ability to deliver intent independently and own end-to-end process from intent creation to delivery High level understanding of machine learning/ RPA automation capabilities Good understanding of various underlying technologies used in RPA such as Artificial Intelligence, Cognitive Computing, Machine Learning, Pattern Matching, Analytics etc. Experience in evaluating various technology products and developing expertise in underlying technology and architecture Certification or training in PMP, PMI-ACP, Scaled Agile Framework (SAFe), Lean Software Development, Scrum and/or Six Sigma Microsoft Office, 365, SharePoint & PowerPoint'guru' Good understanding of various underlying technologies used in RPA such as Artificial Intelligence, Cognitive Computing, Machine Learning, Pattern Matching, Analytics etc.

Ian Goodfellow - Numerical Computation for Deep Learning - AI With The Best Oct 14-15, 2017


AI With The Best hosted 50 speakers and hundreds of attendees from all over the world on a single platform on October 14-15, 2017. The platform held live talks, Insights/Questions pages, and bookings for 1-on-1s with speakers. Ian Goodfellow is a Research Scientist at Google Brain and presented on Numerical Computation for Deep Learning. This presentation covers chapter 4 of the Deep Learning textbook ( Deep learning algorithms are usually described in terms of real numbers, with infinite precision.

15 insights from InsurTech Rising's AI Summit


With AI (Artificial Intelligence) being the one of the hottest topics of 2017 for #InsurTech, here is a quick round-up of some of the takeaway points from the AI Summit that I attended earlier as part of the 2017 InsurTech Rising event. This is interesting, because within the insurance industry, many understand this fact but the distinction between AI, Machine Learning (ML) and Deep Learning (DL) is still misunderstood. AI being so topical is fuelling the misbelief that it is new. ML which is a subset of AI, is where machines learn a function from the data, namely patterns and trends, which we as humans can't always determine ourselves and not as quickly. DL which is a subset of ML, and thus, a subset of AI, is where neural networks (on a much bigger scale) work to think like humans.