The graph represents a network of 4,041 Twitter users whose tweets in the requested range contained "futureofwork ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 14 October 2019 at 01:34 UTC. The requested start date was Monday, 14 October 2019 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 3-day, 8-hour, 54-minute period from Thursday, 10 October 2019 at 15:06 UTC to Monday, 14 October 2019 at 00:00 UTC.

Workers place more trust in robots and AI than their managers according to the second annual AI at Work study conducted by Oracle and Future Workplace. To compile the study, the two firms surveyed 8,370 employees, managers and HR leaders across 10 countries to find that AI has changed the relationship between people and technology in the workplace and is reshaping the role HR teams and managers need to play when it comes to attracting, retaining and developing talent. In contrast to common fears that AI and robots will take workers jobs, the AI at Work study found that employees, managers and HR leaders across the globe are reporting increased adoption of AI in the workplace and many are welcoming the emerging technology with enthusiasm. AI is becoming more prominent in workplaces with 50 percent of workers currently using some form of AI at work compared to only 32 percent last year. Workers in China (77%) and India (78%) have adopted AI over two times more than those in France (32%) and Japan (29%).

Editor's note: Sayak is a speaker for ODSC West in San Francisco this November! Be sure to check out his talk, "Interpretable Machine Learning -- Fairness, Accountability and Transparency in ML systems," there! The problem is it is much harder to evaluate machine learning systems than to train them. "It requires responsibly requires doing more than just calculating loss metrics. Before putting a model into production, it's critical to audit training data and evaluate predictions for bias."

Facebook's head of artificial intelligence, Yann LeCun, seems to fit that profile to a T. "I work mostly by intuition," he writes in When the Machine Learns, a new book that is part biography, part science lecture, part AI history, published Wednesday in French as Quand la machine apprend. "I project in my head the borderline cases, that which Einstein called the'thought experiments'," writes LeCun. LeCun is animated on stage, clearly energized by trying to convey things at the edges of AI that have come from his thought experiments. That ability to imagine something that doesn't exist, perhaps at the limit of what's generally thought feasible, is the mark of engineers and innovators. LeCun is something of a rarity among the AI crowd, a scientist who is at home in algorithm design but also has one foot firmly in computer engineering.

In widening its punishment of China in the ongoing US-China trade war, the US has extended its blacklist to directly handicap its biggest competition in a much higher stakes race, for world domination in AI. Recently, almost ten more AI companies – including providers of video surveillance, facial and speech recognition and data recovery; were added to US trade black list. The reasons cited were related to the violation of human rights by the supposed usage of AI technology in China's repression the Muslim ethnic minority groups of the Uygur region. Here is an interesting digression; that one of the most notable Chinese AI companies in this most recent US blacklist is SenseTime Group (known for its facial recognition AI tech), whose founder Tang Xiao'ou was appointed as the foreign national to Malaysia's sovereign wealth fund, Khazanah Nasional. SenseTime is the top AI'unicorns' startup from China with a valuation of over USD7 billion.

Trees are a low-tech, high-efficiency way to offset much of humankind's negative impact on the climate. What's even better, we have plenty of room for a lot more of them. A new study conducted by researchers at Switzerland's ETH-Zürich, published in Science, details how Earth could support almost an additional billion hectares of trees without the new forests pushing into existing urban or agricultural areas. Once the trees grow to maturity, they could store more than 200 billion metric tons of carbon. Great news indeed, but it still leaves us with some huge unanswered questions. Where and how are we going to plant all the new trees?

One of the companies leading the way is FiO, an innovative new "middleware" ecosystem that has partnered with IBM and is set to help businesses take advantage of the latest blockchain technology. Following Blockchain 1.0 (digital assets) and 2.0 (smart contracts), the approaching Blockchain 3.0 era promises to create compliant independent financial markets and economies that will force businesses to join in order to stay competitive. Yet, constantly evolving technology and exorbitant research and development (R&D) costs make blockchain technology still prohibitively unaffordable for small-to-medium enterprises (SMEs). FiO aims to decrease the barriers to entry and help SMEs integrate blockchain and big data-driven targeted marketing into their businesses. FiO is in its final stages of development and will be completed by the end of October; furthermore, FiO will be online by the end of 2019.

Snapchat is rolling out Dynamic Ads – new ad unit designed specifically for Ecommerce advertisers. "Over the years, Snap has invested heavily in solutions for performance marketers to drive their business, and this new addition will only increase the depth of our offering." What Dynamic Ads offer that Snapchat's other advertising options do not is automated personalization, which offers new ways to scale and drive performance. Snapchat is selling the ad unit as a simple way to create mobile ads at scale while maintaining brand identity through mobile-first templates. Syncing a product catalog allows Snapchat to be continually updated about changes to products.

UC Berkeley Assistant Professor Zachary Pardos and his team have developed a machine learning approach that promises to help more community college students position themselves to transfer and succeed at four-year colleges and universities. Along the way, they've discovered that considering course enrollment patterns -- or the classes that students take before, along with, and after a particular course -- can help provide a more complete picture of what courses should "count" when students transfer. Roughly 80% of community college students aim to continue their education at four-year institutions, but the vast majority never make the transfer. Contributing to the problem are the complexities of "articulation," or determining which course at one institution will count for credit at another. This entails assessing the similarity of thousands, or potentially even millions, of pairs of courses, an endeavor that's impossible to comprehensively achieve and keep current across all institutions manually.

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. He covered examples of how his team addressed business problems with descriptive, predictive, and prescriptive ML solutions. This post provides distilled highlights, a transcript, and a video of the session. In the Rev session, "Data Science at The New York Times", Chris Wiggins provided insights into how the Data Science group at The New York Times helped the newsroom and business be economically strong by developing and deploying ML solutions. Wiggins advised that data scientists ingest business problems, re-frame them as ML tasks, execute on the ML tasks, and then clearly and concisely communicate the results back to the organization. He advocated that an impactful ML solution does not end with Google Slides but becomes "a working API that is hosted or a GUI or some piece of working code that people can put to work". Wiggins also dove into examples of applying unsupervised, supervised, and reinforcement learning to address business problems. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. The data science group, in particular, includes people from a "wide variety of intellectual trainings" including cognitive science, physics, finance, applied math, and more. Wiggins closed the session with indicating how he looks forward to hiring from even more diverse job applications. For more insights from this session, watch the video or read through the transcript. I have about 30 minutes with you. I'm going to try to tell you all about data science at the New York Times, and in case I run out of time my email address and my Twitter are here. If you don't remember anything else, just remember we're hiring.