A detailed look at the latest research from IBM and Forbes Insights around where companies are on their AI journeys and the challenges they’re facing along the way.
By making use of the ‘spooky’ laws behind quantum entanglement, physicists think have found a way to make information leap between a pair of electrons separated by distance.
Teleporting fundamental states between photons – massless particles of light – is quickly becoming old news, a trick we are still learning to exploit in computing and encrypted communications technology.
But what the latest research has achieved is quantum teleportation between particles of matter – electrons –something that could help connect quantum computing with the more traditional electronic kind.
For an indication about bifurcating the levels of self-driving, see my indication here: https://aitrends.com/ai-insider/reframing-ai-levels-for-self…-autonomy/
Conclusion
The consensus among self-driving car aficionados is that a robot driver is a long way away from being practical. A robot driver is considered generally to be more futuristic than trying to develop a self-driving car instead.
The lifecycle starts when data is collected or ingested from any source. With the advent of 5G, this includes ever more data that’s streamed and generated in real-time.
Companies realize that in order to grow, connect products and services, or protect their business, they need to become data-driven. In selecting the tools to realize these goals, organizations effectively have two choices: a self-selected combination of analytics tools and applications or a unified platform that handles all. In this blog we will discuss the challenges of the former choice that will provide justification for the latter. Let’s take a step back and ask: what do organizations need in terms of analytics to realize their data-driven goals? What is needed to combat customer churn, provide a predictive maintenance service or identify fraud as it happens? One thing is clear: it is not one single analytical capability. Implementing innovative and differentiating business use cases is not simply selecting the perfect data warehouse solution and calling it good. Today’s solutions require more than a better individually functional tool. Going from data to insight to action demands a complete range of capabilities that spans the data life cycle from the edge to AI.
The kickoff of Lyft’s second challenge comes months after Waymo expanded its public driving data set and launched the $110,000 Waymo Open Dataset competition. Winners were announced mid-June during a workshop at the 2020 Conference on Computer Vision and Pattern Recognition (CVPR), which was held online this year due to the coronavirus pandemic.
Following the release of the Perception Dataset and the conclusion of its 2019 object detection competition, Lyft today shared a new corpus — the Prediction Dataset — containing the logs of movements of cars, pedestrians, and other obstacles encountered by its fleet of 23 autonomous vehicles in Palo Alto. Coinciding with this, the company plans to launch a challenge that will task entrants with predicting the motion of traffic agents.
A longstanding research problem within the self-driving domain is creating models robust and reliable enough to predict traffic motion. Lyft’s data set focuses on motion prediction by including the movement of traffic types its fleet crossed paths with, like cars, cyclists, and pedestrians. This movement is derived from data collected by the sensor suite mounted to the roof of Lyft’s vehicles, which captures things like lidar and radar readings as the vehicles drive tens of thousands of miles:
Continue reading “Lyft releases new self-driving vehicle data set and launches $30,000 challenge” »
“Facial recognition is a uniquely dangerous form of surveillance. This is not just some Orwellian technology of the future — it’s being used by law enforcement agencies across the country right now, and doing harm to communities right now,” Fight for the Future deputy director Evan Greer said in a statement shared with VentureBeat and posted online.
Members of the United States Congress introduced a bill today, The Facial Recognition and Biometric Technology Moratorium Act of 2020, that would prohibit the use of U.S. federal funds to acquire facial recognition systems or “any biometric surveillance system” use by federal government officials. It would also withhold federal funding through the Byrne grant program for state and local governments that use the technology.
The bill is sponsored by Senators Ed Markey (D-MA) and Jeff Merkley (D-OR) as well as Representatives Ayanna Pressley (D-MA) and Pramila Jayapal (D-WA). Pressley previously introduced a bill prohibiting use of facial recognition in public housing, while Merkley introduced a facial recognition moratorium bill in February with Senator Cory Booker (D-NJ).
Continue reading “Congress introduces bill that bans facial recognition use” »
Is Industry 4.0 applicable to only machines or is there scope for businesses to leverage the concept of ‘Connected Workers’?
The shift towards Industry 4.0 is improving manufacturing efficiency and the factory of the future will increasingly be driven by technology like the Internet of Things (IoT), Automation, Artificial Intelligence (AI), and Cloud Computing.
Alphabet’s Sidewalk Labs plans to spin out some of its smart city ideas into separate companies, CEO Daniel Doctoroff said today at Collision from Home conference. Doctoroff listed three potential companies: mass timber construction, affordable electrification sans fossil fuels, and planning tools optimized with machine learning and computation design.
Last month, Sidewalk Labs killed its Toronto smart city project, which envisioned raincoats designed for buildings, heated pavement, and object-classifying cameras. Privacy advocates celebrated that the Google sister company would not be getting invasive power to surveil residents. But as I argued in my column that week, the story was far from over. Sidewalk Labs was using the COVID-19 pandemic as a scapegoat for the Toronto project, but the company wouldn’t stay idle.
NetHack, which was first released in 1987, is more sophisticated than might be assumed. It tasks players with descending more than 50 dungeon levels to retrieve a magical amulet, during which they must use hundreds of items and fight monsters while contending with rich interactions between the two. Levels in NetHack are procedurally generated and every game is different, which the Facebook researchers note tests the generalization limits of current state-of-the-art AI.
Facebook researchers believe the game NetHack is well-tailored to training, testing, and evaluating AI models. Today, they released the NetHack Learning Environment, a research tool for benchmarking the robustness and generalization of reinforcement learning agents.
For decades, games have served as benchmarks for AI. But things really kicked into gear in 2013 — the year Google subsidiary DeepMind demonstrated an AI system that could play Pong, Breakout, Space Invaders, Seaquest, Beamrider, Enduro, and Q*bert at superhuman levels. The advancements aren’t merely improving game design, according to folks like DeepMind cofounder Demis Hassabis. Rather, they’re informing the development of systems that might one day diagnose illnesses, predict complicated protein structures, and segment CT scans.
Pagaya, an AI-driven institutional asset manager that focuses on fixed income and consumer credit markets, today announced it raised $102 million in equity financing. CEO Gal Krubiner said the infusion will enable Pagaya to grow its data science team, accelerate R&D, and continue its pursuit of new asset classes including real estate, auto loans, mortgages, and corporate credit.
Pagaya applies machine intelligence to securitization — the conversion of an asset (usually a loan) into marketable securities (e.g., mortgage-backed securities) that are sold to other investors — and loan collateralization. It eschews the traditional method of securitizing pools of previously assembled asset-backed securities (ABS) for a more bespoke approach, employing algorithms to compile discretionary funds for institutional investors such as pension funds, insurance companies, and banks. Pagaya selects and buys individual loans by analyzing emerging alternative asset classes, after which it assesses their risk and draws on “millions” of signals to predict their returns.
Pagaya’s data scientists can build algorithms to track activities, such as auto loans made to residents in cities and even specific neighborhoods, for instance. The company is only limited by the amount of data publicly available; on average, Pagaya looks at decades of information on borrowers and evaluates thousands of variables.
