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Don’t want a robot stealing your job? Take a course on AI and machine learning.

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There are some 288 lessons included in this online training course.
There are some 288 lessons included in this online training course.
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TL;DR: Jump into the world of AI with the Essential AI and Machine Learning Certification Training Bundle for $39.99, a 93% savings. 


From facial recognition to self-driving vehicles, machine learning is taking over modern life as we know it. It may not be the flying cars and world-dominating robots we envisioned 2020 would hold, but it’s still pretty futuristic and frightening. The good news is if you’re one of the pros making these smart systems and machines, you’re in good shape. And you can get your foot in the door by learning the basics with this Essential AI and Machine Learning Certification Training Bundle.

This training bundle provides four comprehensive courses introducing you to the world of artificial intelligence and machine learning. And right now, you can get the entire thing for just $39.99.

These courses cover natural language processing, computer vision, data visualization, and artificial intelligence basics, and will ultimately teach you to build machines that learn as they’re fed human input. Through hands-on case studies, practice modules, and real-time projects, you’ll delve into the world of intelligent systems and machines and get ahead of the robot revolution.

Here’s what you can expect from each course:

Artificial Intelligence (AI) & Machine Learning (ML) Foundation Course

Access 72 lectures and six hours of content exploring topics like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep architectures using TensorFlow. Ultimately, you’ll build a foundation in both artificial intelligence, which is the concept in which machines develop the ability to simulate natural intelligence to carry out tasks, and machine learning, which is an application of AI aiming to learn from data and build on it to maximize performance.

Data Visualization with Python and Matplotlib Training Course

Through seven hours of content, you’ll learn how to arrange critical data in a visual format — think graphs, charts, and pictograms. You’ll also learn to deploy data visualization through Python using Matplotlib, a library that helps in viewing the data. Finally, you’ll tackle actual geographical plotting using the Matplotlib extension called Basemap.

Computer Vision Training Course

In just 5.5 hours, this course gives you a more in-depth look at the role of CNNs, the knowledge of transfer learning, object localization, object detection, and using TensorFlow. You’ll also learn the challenges of working with real-world data and how to tackle them head-on.

Natural Language Processing Training Course

Natural language processing (NLP) is a field of AI which allows machines to interpret and comprehend human language. Through 5.5 hours of content, you’ll understand the processes involved in this field and learn how to build artificial intelligence for automation. The course itself provides an innovative methodology and sample exercises to help you dive deep into NLP.

Originally $656, you can slash 93% off and get a year’s worth of access to the Essential AI and Machine Learning Bundle for just $39.99 right now.

Prices subject to change.

Don't want a robot stealing your job? Take a course on AI and machine learning.

Source: http://feeds.mashable.com/~r/mashable/tech/~3/vtuPPQSi8rg/

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Feds Are Content to Let Cars Drive, and Regulate, Themselves

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The Trump administration Wednesday reaffirmed its policy to maintain a light touch in regulating self-driving vehicles, with a new document that is long on promoting the industry and silent on rules governing testing or operating the vehicles. “The takeaway from the [new policy] is that the federal government is all in” on automated driving systems, US transportation secretary Elaine Chao told an audience at CES in Las Vegas, where she announced the update.

Currently, the federal government offers voluntary safety guidelines for the 80-odd developers working on self-driving vehicles in the US, and it leaves most regulation to the states. Despite calls from some safety advocates—including the National Transportation Safety Board, following a fatal 2018 crash involving an Uber self-driving car—the updated policy doesn’t set out regulations for the tech. The Transportation Department has said it’s waiting for guidance from Congress, which has so far failed to pass any legislation related to self-driving vehicles.

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The new policy seeks to demonstrate that the US government is firmly in developers’ corner. It outlines how the Trump administration has worked across 38 federal agencies—including the departments of Agriculture, Defense, and Energy, the White House, NASA, and the United States Postal Service—to unify its approach to self-driving, and to point billions towards its research and development. It says the government will help protect sensitive, AV-related intellectual property, and outlines tax incentives to those working on self-driving tech in the US. It also emphasizes the need for a unified industry approach to cybersecurity and consumer privacy. The DOT says it will publish a “comprehensive plan” for safe deployment of self-driving vehicles in the US sometime this year.

A full-speed-ahead approach is needed, Chao said, because “automated vehicles have the potential to save thousands of lives, annually.” Unlike humans, robots don’t get drunk, tired, or distracted (though they have lots of learning to do before they can be deployed on a wide scale). According to government data, 36,560 people died in highway crashes in 2018, 2.4 percent fewer than the prior year. Developers often argue it's too soon to regulate self-driving vehicles because the tech is still immature.

The policy reflects the light and tech-neutral touch the Trump administration has generally taken with developing tech, even as fears about surveillance and privacy swirl. Also on Wednesday at CES, US chief technology officer Michael Kratsios outlined the administration’s approach to artificial intelligence, which calls for development supported by “American values” and a process of “risk assessment and cost-benefit analyses” before regulatory action.

LEARN MORE
The WIRED Guide to Self-Driving Cars

In the US, states have taken the lead in regulating the testing of self-driving vehicles, and they are demanding varying levels of transparency from companies like Waymo, Cruise, Uber, and Aurora that are operating on public roads. (The Transportation Department has said that it provides technical assistance to state regulators.) As a result, no one has a crystal clear picture of where testing is happening, or how the tech is developing overall. (Waymo, which is currently carrying a limited number of paying passengers in totally driverless vehicles in metro Phoenix, is widely thought to be in the lead.) The National Highway Traffic Safety Administration, the federal government’s official auto regulator, has politely asked each developer to conduct a voluntary safety self-assessment and outline its approach to safety. But just 18 companies have submitted those assessments so far, and the quality of information within them ranges widely.

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Not all road safety advocates are pleased with that approach. “The DOT is supposed to ensure that the US has the safest transportation system in the world, but it continues to put this mission second, behind helping industry rush automated vehicles,” Ethan Douglas, a senior policy analyst for cars and product safety at Consumer Reports, said in a statement.

Some calls are coming from within the US government. In November, the National Transportation Safety Board released its final report on a fatal 2018 collision between a testing Uber self-driving vehicle and an Arizona pedestrian crossing a road. The watchdog agency’s recommendations included calls to make the safety assessments mandatory and to set up a system through which NHTSA might evaluate them. “We’re just trying to put some bounds on the testing on the roadways,” NTSB chair Robert Sumwalt said. At the time, NHTSA said it would “carefully review” the recommendations.


Read more: https://www.wired.com/story/feds-content-cars-drive-regulate-themselves/

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Two Sigma Ventures raises $288M, complementing its $60B hedge fund parent

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Eight years ago, Two Sigma Investments began an experiment in early-stage investing.

The hedge fund, focused on data-driven quantitative investing, was well on its way to amassing the $60 billion in assets under management that it currently holds, but wanted more exposure to early-stage technology companies, so it created a venture capital arm, Two Sigma Ventures.

At the time of the firm’s launch it made a series of investments, totaling about $70 million, exclusively with internal capital. The second fund was a $150 million vehicle that was backed primarily by the hedge fund, but included a few external limited partners.

Now, eight years and several investments later, the firm has raised $288 million in new funding from outside investors and is pushing to prove out its model, which leverages its parent company’s network of 1,700 data scientists, engineers and industry experts to support development inside its portfolio.

The world is becoming awash in data and there’s continuing advances in the science of computing,” says Two Sigma Ventures co-founder Colin Beirne. “We thought eight years ago when when started, that more and more companies of the future would be tapping into those trends.”

Beirne describes the firm’s investment thesis as being centered on backing data-driven companies across any sector — from consumer technology companies like the social networking monitoring application, Bark, or the high-performance, high-end sports wearable company, Whoop.

Alongside Beirne, Two Sigma Ventures is led by three other partners: Dan Abelon, who co-founded SpeedDate and sold it to IAC; Lindsey Gray, who launched and led NYU’s Entrepreneurial Institute; and Villi Iltchev, a former general partner at August Capital.

Recent investments in the firm’s portfolio include Firedome, an endpoint security company; NewtonX, which provides a database of experts; Radar, a location-based data analysis company; and Terray Therapeutics, which uses machine learning for drug discovery.

Other companies in the firm’s portfolio are farther afield. These include the New York-based Amper Music, which uses machine learning to make new music; and Zymergen, which uses machine learning and big data to identify genetic variations useful in pharmaceutical and industrial manufacturing.

Currently, the firm’s portfolio is divided between enterprise investments, consumer-facing deals and healthcare-focused technologies. The biggest bucket is enterprise software companies, which Beirne estimates represents about 65% of the portfolio. He expects the firm to become more active in healthcare investments going forward.

“We really think that the intersection of data and biology is going to change how healthcare is delivered,” Beirne says. “That looks dramatically different a decade from now.”

To seed the market for investments, the firm’s partners have also backed the Allen Institute’s investment fund for artificial intelligence startups.

Together with Sequoia, KPCB and Madrona, Two Sigma recently invested in a $10 million financing to seed companies that are working with AI. “This is a strategic investment from partner capital,” says Beirne.

Typically startups can expect Two Sigma to invest between $5 million and $10 million with its initial commitment. The firm will commit up to roughly $15 million in its portfolio companies over time.

Read more: https://techcrunch.com/2020/01/22/two-sigma-ventures-raises-288-million-complementing-its-60-billion-hedge-fund-parent/

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German football league Bundesliga teams with AWS to improve fan experience

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Germany’s top soccer (football) league, Bundesliga, announced today it is partnering with AWS to use artificial intelligence to enhance the fan experience during games.

Andreas Heyden, executive vice president for digital sports at the Deutsche Fußball Liga, the entity that runs Bundesliga, says that this could take many forms, depending on whether the fan is watching a broadcast of the game or interacting online.

“We try to use technology in a way to excite a fan more, to engage a fan more, to really take the fan experience to the next level, to show relevant stats at the relevant time through broadcasting, in apps and on the web to personalize the customer experience,” Heyden said.

This could involve delivering personalized content. “In times like this when attention spans are shrinking, when a user opens up the app the first message should be the most relevant message in that context in that time for the specific user,” he said.

It also can help provide advanced statistics to fans in real time, even going so far as to predict the probability of a goal being scored at any particular moment in a game that would have an impact on your team. Heyden thinks of it as telling a story with numbers, rather than reporting what happened after the fact.

“We want to, with the help of technology, tell stories that could not have been told without the technology. There’s no chance that a reporter could come up with a number of what the probability of a shot [scoring in a given moment]. AWS can,” he said.

Werner Vogels, CTO at Amazon, says this about using machine learning and other technologies on the AWS platform to add to the experience of watching the game, which should help attract younger fans, regardless of the sport. “All of these kind of augmented customer fan experiences are crucial in engaging a whole new generation of fans,” Vogels told TechCrunch.

He adds that this kind of experience simply wasn’t possible until recently because the technology didn’t exist. “These things were impossible five or 10 years ago, mostly because now with all the machine learning software, as well as how the [pace of technology] has accelerated at such a [rate] at AWS, we’re now able to do these things in real time for sports fans.”

Bundesliga is not just any football league. It is the second biggest in the world in terms of revenue, and boasts the highest stadium attendance of all football teams worldwide. Today’s announcement is an extension of an ongoing relationship between DFL and AWS, which started in 2015 when Heyden helped move the league’s operations to the cloud on AWS.

Heyden says that it’s not a coincidence he ended up using AWS instead of another cloud company. He has known Vogels (who also happens to be a huge soccer fan) for many years, and has been using AWS for more than a decade, even well before he joined the DFL. Today’s announcement is an extension of that long-term relationship.

Read more: https://techcrunch.com/2020/01/24/german-football-league-bundesliga-teams-with-aws-to-improve-fan-experience/

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