UPDATE Oct, 2019: We just added a new season with 4 new podcasts focused on artificial intelligence, machine learning, data science, and data orchestration. Building a data foundation is essential to driving innovation. This is just as true for mid-market companies as for large enterprise companies. Mid-market and large enterprise companies have different challenges, so we've brought together experts from each size company to discuss key trends that are reshaping the way successful companies use their data: from data management and data foundation to spatial and machine learning to data-based process and information excellence. Listen to this chat series on all things data!
Are you sure you want to view these Tweets? Agreed, and appreciate the parallel drawn here. Definitely a huge challenge to regulate these emerging & booming sectors. Interesting reading this as well: «I have been proud to work with #Tesla on advancing cleaner, more #sustainable #transportation technologies. Impact of #Digitalization and #Automation, #futureofwork "This is your #pilot speaking.
An estimated 130 people die from opioid-related drug overdoses each day in the United States, and 2 million people had an opioid use disorder in 2018. This public health crisis has left officials scrambling for ways to cut down on illegal sales of these controlled substances, including online sales. Now the National Institute on Drug Abuse, which is part of the US Department of Health and Human Services, is investing in an artificial intelligence-based tool to track how "digital drug dealers" and illegal internet pharmacies market and sell opioids (though online transactions are likely not a large share of overall illegal sales). New AI-based approaches to clamping down on illegal opioid sales demonstrate how publicly available social media and internet data -- even the stuff you post -- can be used to find illegal transactions initiated online. It could also be used to track just about anything else, too: The researcher commissioned by NIDA to build this tool, UC San Diego professor Timothy Mackey, told Recode the same approach could be used to find online transactions associated with illegal wildlife traffickers, vaping products, counterfeit luxury products, and gun sales.
The algorithm lets robots find the shortest route in unfamiliar environments, opening the door to robots that can work inside homes and offices. The news: A team at Facebook AI has created a reinforcement learning algorithm that lets a robot find its way in an unfamiliar environment without using a map. Using just a depth-sensing camera, GPS, and compass data, the algorithm gets a robot to its goal 99.9% of the time along a route that is very close to the shortest possible path, which means no wrong turns, no backtracking, and no exploration. This is a big improvement over previous best efforts. Why it matters: Mapless route-finding is essential for next-gen robots like autonomous delivery drones or robots that work inside homes and offices.
Rogue NYPD officers are using a sketchy facial recognition software on their personal phones that the department's own facial recognition unit doesn't want to touch because of concerns about security and potential for abuse, The Post has learned. Clearview AI, which has scraped millions of photos from social media and other public sources for its facial recognition program -- earning a cease-and-desist order from Twitter -- has been pitching itself to law enforcement organizations across the country, including to the NYPD. The department's facial recognition unit tried out the app in early 2019 as part of a complimentary 90-day trial but ultimately passed on it, citing a variety of concerns. Those include app creator Hoan Ton-That's ties to viddyho.com, which was involved in a widespread phishing scam in 2009, according to police sources and reports. The NYPD was also concerned because Clearview could not say who had access to images once police loaded them into the company's massive database, sources said.
When trying to understand time series, there's so much to think about. Is it affected by seasonality? What kind of model should I use, and how well will it perform? All these questions can make time series modeling kind of intimidating, but it doesn't have to be that bad. While working on a project for my data science bootcamp recently, I tried Facebook Prophet, an open-source package for time series modeling developed by … y'know, Facebook.
Facebook has scored an impressive feat involving AI that can navigate without any map. Facebook's wish for bragging rights, although they said they have a way to go, were evident in its blog post, "Near-perfect point-goal navigation from 2.5 billion frames of experience." Long story short, Facebook has delivered an algorithm that, quoting MIT Technology Review, lets robots find the shortest route in unfamiliar environments, opening the door to robots that can work inside homes and offices." And, in line with the plain-and-simple, Ubergizmo's Tyler Lee also remarked: "Facebook believes that with this new algorithm, it will be capable of creating robots that can navigate an area without the need for maps...in theory, you could place a robot in a room or an area without a map and it should be able to find its way to its destination." Erik Wijmans and Abhishek Kadian in the Facebook Jan. 21 post said that, well, after all, one of the technology key challenges is "teaching these systems to navigate through complex, unfamiliar real-world environments to reach a specified destination--without a preprovided map." Facebook has taken on the challenge. The two announced that Facebook AI created a large-scale distributed reinforcement learning algorithm called DD-PPO, "which has effectively solved the task of point-goal navigation using only an RGB-D camera, GPS, and compass data," they wrote. DD-PPO stands for decentralized distributed proximal policy optimization. This is what Facebook is using to train agents and results seen in virtual environments such as houses and office buildings were encouraging. The bloggers pointed out that "even failing 1 out of 100 times is not acceptable in the physical world, where a robot agent might damage itself or its surroundings by making an error." Beyond DD-PPO, the authors gave credit to Facebook AI's open source AI Habitat platform for its "state-of-the-art speed and fidelity." AI Habitat made its open source announcement last year as a simulation platform to train embodied agents such as virtual robots in photo-realistic 3-D environments. Facebook said it was part of "Facebook AI's ongoing effort to create systems that are less reliant on large annotated data sets used for supervised training." InfoQ had said in July that "The technology was taking a different approach than relying upon static data sets which other researchers have traditionally used and that Facebook decided to open-source this technology to move this subfield forward." Jon Fingas in Engadget looked at how the team worked toward AI navigation (and this is where that 25 billion number comes in). "Previous projects tend to struggle without massive computational power.
MADS East 2019 was a two-day conference in December that gave attendees endless opportunities to expose themselves to new ideas in the space of data science for marketing. Some of this year's conference perks included: tables for one-on-one networking, a half-an-hour off the record roundtable with 7 industry leaders, two unique tracks per day, buffet-style lunches, breakfasts, snacks, a refreshing break for cocktails at the Opening Night Party, and NYC Times Square views. This article is my summary of the Day 1 presentations I was able to attend, including lessons and reminders from the speakers. Aside from staying up to date on industry trends, MADS East has also proven itself a valuable opportunity for data and marketing people who are looking to engage with professionals of varying career levels. I was expecting to be the only individual with little background in data or extended industry experience present, but to my surprise, there was a decent balance between early, mid and late-career attendees.
Way back in May 2011, Eric Schmidt, who was then the executive chairman of Google, said that the rapid development of facial recognition technology had been one of the things that had surprised him most in a long career as a computer scientist. But its "surprising accuracy" was "very concerning". Questioned about this, he said that a database using facial recognition technology was unlikely to be a service that the company would create, but went on to say that "some company … is going to cross that line". As it happens, Dr Schmidt was being economical with the actualité, as the MP Alan Clark used to say. He must surely have known that a few months earlier Facebook had announced that it was using facial recognition in the US to suggest names while tagging photos.