Hoo-boy! This damning Uber letter is a wild ride


The disasters that former Uber CEO Travis Kalanick left in his wake at his popular ride-hailing app company was one of this year's biggest tech industry stories. Now, as we wrap up the year, Uber (through a court case) has gifted us a letter detailing many of the company's alleged wrongdoings and spy tactics. The so-called Jacobs letter was written by an attorney representing Richard Jacobs, a former Uber security analyst. It alleges shady and potential illegal operations, including how Uber employees monitored the competition and acquired trade secrets. SEE ALSO: Uber's new CEO says he banned employees from using secure messaging apps for Uber business The letter is among the evidence in the trial between Uber and Waymo, Alphabet's self-driving car division.

ELIZA - Wikipedia


ELIZA is an early natural language processing computer program created from 1964 to 1966[1] at the MIT Artificial Intelligence Laboratory by Joseph Weizenbaum.[2] Created to demonstrate the superficiality of communication between humans and machines, Eliza simulated conversation by using a'pattern matching' and substitution methodology that gave users an illusion of understanding on the part of the program, but had no built in framework for contextualizing events.[3] Directives on how to interact were provided by'scripts', written originally in MAD-Slip, which allowed ELIZA to process user inputs and engage in discourse following the rules and directions of the script. The most famous script, DOCTOR, simulated a Rogerian psychotherapist and used rules, dictated in the script, to respond with non-directional questions to user inputs. As such, ELIZA was one of the first chatterbots, but was also regarded as one of the first programs capable of passing the Turing Test.[clarification needed] ELIZA's creator, Weizenbaum regarded the program as a method to show the superficiality of communication between man and machine, but was surprised by the number of individuals who attributed human-like feelings to the computer program, including Weizenbaum's secretary.[2] Many academics believed that the program would be able to positively influence the lives of many people, particularly those suffering from psychological issues and that it could aid doctors working on such patients' treatment.[2][4]

Bringing Machine Learning (TensorFlow) to the enterprise with SAP HANA


In this blog I aim to provide an introduction to TensorFlow and the SAP HANA integration, give you an understanding of the landscape and outline the process for using External Machine Learning with HANA. There's plenty of hype around Machine Learning, Deep Learning and of course Artificial Intelligence (AI), but understanding the benefits in an enterprise context can be more challenging. Being able to integrate the latest and greatest deep learning models into your enterprise via a high performance in-memory platform could provide a competitive advantage or perhaps just keep up with the competition? With HANA 2.0 SP2 onwards we have the ability to call TensorFlow (TF) models or graphs as they are known. HANA now includes a method to call External Machine Learning (EML) models via a remote source.

Machine Learning Engineer - Video


Muse is creating an advanced AI to search the world's video. We are looking for engineers who want to be at the forefront of search and discovery to make searching video as intuitive as recalling a memory in your mind. We are working across the frontiers of storage & distributed file systems, machine learning infrastructure, high performance computing and intuitive user interfaces across (web, mobile, voice and beyond). You will join the machine learning team in sunny Lisbon with a focus on implementing and productionizing the latest machine learning algorithms to analyze video and interpret the knowledge inside video across speech, people, objects, actions and locations. We are agnostic to the algorithms, but care deeply about the computational efficiency of these algorithms in the analysis of a video file.

Scopes of Machine Learning and Artificial Intelligence in Banking & Financial Services ML & AI - The Future of Fintechs


Whether financial institutions are looking for improved customer service, risk management, fraud prevention, investment prediction or cybersecurity, the scopes of machine learning and artificial intelligence are limitless. In the modern era of the digital economy, technological advancements are no longer a luxury for the organizations, but a necessity to outsmart their competitors and business growth. With the technological advancements in the recent times, the impact of Machine Learning (ML) and Artificial Intelligence (AI) are very critical than ever before. Previously, we discussed the scopes of big data and data science in banking and financial services. In this article will explain in detail about ML and AI, and their scopes in banking and financial services.

10 Key Big Data Trends That Drove 2017


It was a memorable year, to be sure, with plenty of drama and unexpected happenings in terms of the technology, the players, and the application of big data and data science. As we gear up for 2018, we think it's worth taking some time to ponder about what happened in 2017 and put things in some kind of order. Here are 10 of the biggest takeaways for the big data year that was 2017. Teradata, for instance, found that 80% of enterprises are already investing in AI, which backed similar findings from IDC. Nevertheless, the same old challenges that kept big data off Easy Street also emerged to cool some of the heat emanating from AI.

Blockchain-powered medical AI Skychain promises to beat IBM's Watson Health


Another important distinction is Skychain's use of distributed computing technologies based on the blockchain principles. Many thousands of crypto miners will provide their computational resources to Skychain to get a reward each time an independent neural network performs calculations at the request of an end user, or each time a neural network is trained at the request of its developer. It means that Skychain's end users and neural network developers won't need to bother about obtaining any special hardware or computational resources.

Deep Learning Joins Process Control Arsenal Semiconductor Manufacturing & Design Community


At the 2017 Advanced Process Control (APC 2017) conference, several companies presented implementations of deep learning to find transistor defects, align lithography steps, and apply predictive maintenance. The application of neural networks to semiconductor manufacturing was a much-discussed trend at the 2017 APC meeting in Austin, starting out with a keynote speech by Howard Witham, Texas operations manager for Qorvo Inc. Witham said artificial intelligence has brought human beings to "a point in history, for our industry and the world in general, that is more revolutionary than a small, evolutionary step." People in the semiconductor industry "need to take what's out there and figure out how to apply it to your own problems, to figure out where does the machine win, and where does the brain still win?" At Seagate Technology, a small team of engineers stitched together largely packaged or open source software running on a conventional CPU to create a convolution neural network (CNN)-based tool to find low-level device defects. In an APC paper entitled Automated Wafer Image Review using Deep Learning, Sharath Kumar Dhamodaran, an engineer/data scientist based at Seagate's Bloomington, Minn.

IBM Power9 bulks up for AI workloads


The latest proprietary Power servers from IBM, armed by the long-awaited IBM Power9 processors, look for relevance among next-generation enterprise workloads, but the company will need some help from its friends to take on its biggest market challenger. IBM emphasizes increased speed and bandwidth with its AC922 Power Systems to better take on high-performance computing tasks, such as building models for AI and machine learning training. The company said it plans to pursue mainstream commercial applications, such as building supply chains and medical diagnostics, but those broader-based opportunities may take longer to materialize. "Most big enterprises are doing research and development on machine learning, with some even deploying such projects in niche areas," said Patrick Moorhead, president and principal analyst at Moor Insights & Strategy. "But it will be 12 to 18 months before enterprises can even start driving serious volume in that space."

How AI Will Make Marketing More Personalized in 2018 Marketing Insider Group


AI is making the customer experience more personalized than what most marketers would have ever thought to be possible. Companies are already using artificial intelligence to make their websites, emails, social media posts, video and other content better tailored to what customers want right now. This is going to re-energize the push towards higher customer expectations even more, leading to not just steps forward in the way brands interact with their customers, but leaps ahead. How can you keep pace with this exponential change? Start adopting the AI methods that are relevant to your industry, and to your business, today.