In IEEE 802.11 WiFi-based waveforms, the receiver performs coarse time and frequency synchronization using the first field of the preamble known as the legacy short training field (L-STF). The L-STF occupies upto 40% of the preamble length and takes upto 32 us of airtime. With the goal of reducing communication overhead, we propose a modified waveform, where the preamble length is reduced by eliminating the L-STF. To decode this modified waveform, we propose a neural network (NN)-based scheme called PRONTO that performs coarse time and frequency estimations using other preamble fields, specifically the legacy long training field (L-LTF). Our contributions are threefold: (i) We present PRONTO featuring customized convolutional neural networks (CNNs) for packet detection and coarse carrier frequency offset (CFO) estimation, along with data augmentation steps for robust training. (ii) We propose a generalized decision flow that makes PRONTO compatible with legacy waveforms that include the standard L-STF. (iii) We validate the outcomes on an over-the-air WiFi dataset from a testbed of software defined radios (SDRs). Our evaluations show that PRONTO can perform packet detection with 100% accuracy, and coarse CFO estimation with errors as small as 3%. We demonstrate that PRONTO provides upto 40% preamble length reduction with no bit error rate (BER) degradation. We further show that PRONTO is able to achieve the same performance in new environments without the need to re-train the CNNs. Finally, we experimentally show the speedup achieved by PRONTO through GPU parallelization over the corresponding CPU-only implementations.

When Bradford Newman began advocating for more artificial intelligence expertise in the C-suite in 2015, "people were laughing at me," he said. Newman, who leads global law firm Baker McKenzie's machine learning and AI practice in its Palo Alto office, added that when he mentioned the need for companies to appoint a chief AI officer, people typically responded, "What's that?" But as the use of artificial intelligence proliferates across the enterprise, and as issues around AI ethics, bias, risk, regulation and legislation currently swirl throughout the business landscape, the importance of appointing a chief AI officer is clearer than ever, he said. This recognition led to a new Baker McKenzie report, released in March, called "Risky Business: Identifying Blind Spots in Corporate Oversight of Artificial Intelligence." The report surveyed 500 US-based, C-level executives who self-identified as part of the decision-making team responsible for their organization's adoption, use and management of AI-enabled tools. In a press release upon the survey's release, Newman said: "Given the increase in state legislation and regulatory enforcement, companies need to step up their game when it comes to AI oversight and governance to ensure their AI is ethical and protect themselves from liability by managing their exposure to risk accordingly."

Anthony Levandowski, the controversial engineer at the heart of a lawsuit between Uber and Waymo, claims to have built an automated car that drove from San Francisco to New York without any human intervention. The 3,099-mile journey started on 26 October on the Golden Gate Bridge, and finished nearly four days later on the George Washington Bridge in Manhattan. The car, a modified Toyota Prius, used only video cameras, computers and basic digital maps to make the cross-country trip. Levandowski told the Guardian that, although he was sitting in the driver's seat the entire time, he did not touch the steering wheels or pedals, aside from planned stops to rest and refuel. "If there was nobody in the car, it would have worked," he said.

The controversial engineer at the center of Uber's multi-year row with Waymo claims he has completed the longest coast-to-coast trip in a self-driving car across the U.S. Anthony Levandowski, a former Uber engineer, told the Guardian that he didn't touch the autonomous vehicle's steering wheel or pedals during the four-day, 3,099-mile trip from San Francisco to New York City, aside from the occasional rest stop. While the Guardian didn't confirm the details of his trip, if it occurred as Levandowski described, it marks the longest recorded trip by a self-driving car without a human taking over. Levandowski rode in a modified Toyota Prius for the 3,099-mile trip from San Francisco to New York City. The car operates using a semi-autonomous driver-assistance system, named Co-Pilot. Co-Pilot is a level two autonomous system.

Anthony Levandowski, the engineer whose alleged theft of trade secrets landed him in the middle of a blockbuster self-driving car legal fight, has stepped back into the spotlight with a new company. Pronto AI, he announced on Tuesday, is developing a $5,000 aftermarket driver assistance system for semitrucks, which will handle the steering, throttle, and brakes on the highway. To prove it works, Levandowski used the software to send his Toyota Prius across the country. In October, Levandowski says, the car drove 3,099 miles from San Francisco to New York City. At no point did he take control away from the computer, except to handle the non-freeway bits, chiefly to refuel and rest up.

Remember how controversial former Uber engineer Anthony Levandowski had formed a secretive autonomous trucking startup? He's finally showing off his work... and he might have set a record in the process. Levandowski has launched his self-driving truck startup Pronto.AI by posting a video (below) that appears to show him traveling 3,099 miles from San Francisco to New York City in an AI-augmented Prius "without any human intervention" or pre-mapping, and only a small amount of training. The entrepreneur only had to take over when it was time to refill the car and rest up, according to his interview with The Guardian. It's focusing on Copilot, a driver assistance system for trucks that offers the lane keeping, adaptive cruise control and collision prevention that you see in some newer cars.

Remember those little six-wheeled robots we told you about in April? They're now set for a commercial rollout in London and three other European cities. The robots, from Starship Technologies, will be deployed this month to make deliveries for food-ordering services Just Eat and Pronto, and carry packages for courier service Hermes and supermarket Metro Group. The Starship delivery bots will be stationed at kitchens, delivery hubs, and supermarkets in London, Düsseldorf, Bern, and Hamburg. When an order comes in, the bots will drive themselves to collect their cargo, store it in their holds (which can take about two shopping bags' worth of stuff), then trundle on to their destinations.

Self-driving robots will soon start delivering food and groceries. This month Starship Technologies is rolling out its six-wheeled delivery robots in London, Dusseldorf, Bern, and Hamburg, Quartz reports. The robots will be used by two food delivery services in those areas, Just Eat and Pronto, as well as courier service Hermes and grocery store Metro Group. Starship hopes that this new technology will help cut both the time and costs associated with delivery. The self-driving robots won't exactly be self-driving at first.