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Category: robotics/AI

We are currently witnessing an explosion of network traffic. Numerous emerging services and applications, such as cloud services, video streaming platforms and the Internet of Things (IOT), are further increasing the demand for high-capacity communications. Optical communication systems, technologies that transfer information optically using fibers, are the backbone of today’s communication networks of fixed-line, wireless infrastructure and data centers.

Over the past decade, the growth of the internet was enabled by a technique known as digital signal processing (DSP), which can help to reduce transmission distortions. However, DSP is currently implemented using CMOS integrated circuits (ICs), thus it relies heavily on Moore’s Law, which has approached its limits in terms of power dissipation, density and feasible engineering solutions.

As a result, distortions caused by a phenomenon known as fiber nonlinearity cannot be compensated by DSP, as this would require too much computation power and resources. Fiber nonlinearities remain the major limiting effect on long-distance transmission systems.

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This article was contributed by Valerias Bangert, strategy and innovation consultant, founder of three media outlets, and published author.

AI job automation: The debate

The debate around whether AI will automate jobs away is heating up. AI critics claim that these statistical models lack the creativity and intuition of human workers and that they are thus doomed to specific, repetitive tasks. However, this pessimism fundamentally underestimates the power of AI. While AI job automation has already replaced around 400,000 factory jobs in the U.S. from 1990 to 2007, with another 2 million on the way, AI today is automating the economy in a much more subtle way.

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Recharging Drones in only 45 minutes.

Recently, Autel Robotics released a new drone charging platform that allows drones to take on multiple recursive missions independent of weather across a wide variety of industrial applications, including industrial energy inspection, natural disaster monitoring, and more.

But another tilt-rotor VTOL drone from Autel can transition to a “fixed-wing” mode, and “scout areas after a hurricane, with a lot of different really high-end camera options,” said John Simmons, a representative for Autel Robotics at the CES 2022 exhibit.

The drone charging platform is called EVO Nest, while the long-range, fixed-wing VTOL is called the “Dragonfish” series. And it could simplify the energy needs of visual surveillance, monitoring, and public service.

Full Story:

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The Pentagon, the CIA, and the State Department are already using the technology.

Who can forget the attack on Capital last January 6th? For those who do remember it well, there is an urgency to do something to avoid it ever happening again. One way to do that is to predict these events before they happen just like you can predict weather patterns.

Some data scientists believe they can achieve exactly that, according to The Washington Post. “We now have the data — and opportunity — to pursue a very different path than we did before,” said Clayton Besaw, who helps run CoupCast, a machine-learning-driven program based at the University of Central Florida that predicts coups for a variety of countries.

This type of predictive modeling has been around for a while but has mostly focused on countries where political unrest is far more common. Now, the hope is that it can be redirected to other nations to help prevent events like that of January 6th. And so far, the firms working in this field have been quite successful.

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The vehicle showcased at the event was Model SD-03, which was a demonstration for the autonomous SD-05 which is currently under development. The company is aiming to kickstart its business with the latter after unveiling it as a flying taxi at the World Expo 2025 in Osaka. It is worth mentioning that SkyDrive has been tested for manned flights and recently got certified by the Japanese government. “SkyDrive recently advanced toward commercialization with the Japanese transportation ministry’s acceptance of its type certificate application, a major milestone that no other flying vehicle developers have reached in Japan”, the company said in its statement.

READ | Flying car completes first 35-minute inter-city flight test in Slovakia

The model released by SkyDrive at the CES 2022 is a driver-only vehicle that runs on electricity and is equipped with eight propellers. As per SkyDrive’s description of the vehicle, it can carry a maximum weight of 400 kg and is capable of cruising at 40–50 kilometres per hour for five to ten minutes. The company had revealed the first prototype of its eVTOL in 2018 and conducted the first manned flight in 2020. According to a report by Interesting Engineering, more companies such as Lilium and Volocopter are also planning to kickstart their flying car business this decade.

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China’s technology giant Baidu is stepping up its efforts to expand in the autonomous vehicle segment with the commercial launch of a car model with Level-2 self-driving technology next year.

Last week the company’s CEO Robin Li confirmed that Jidu Auto, Baidu’s joint venture with local automaker Zhejiang Geely Holding Group, plans to begin mass production of its first electric vehicle (EV) with Level-2 autonomous driving technologies in 2023. The vehicle’s self-driving system is powered by Nvidia chips and is scheduled to be unveiled at the Beijing Auto Show in April of this year.

Baidu, known widely as an internet search engine and artificial intelligence company, is targeting the autonomous vehicle segment as a key growth industry and is in the process of rolling out autonomous taxi services across China.

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Chief information security officers’ (CISOs) greatest challenge going into 2022 is countering the speed and severity of cyberattacks. The latest real-time monitoring and detection technologies improve the odds of thwarting an attack but aren’t foolproof. CISOs tell VentureBeat that bad actors avoid detection with first-line monitoring systems by modifying attacks on the fly. That’s cause for concern, especially with CISOs in financial services and health care.

Enterprises are in react mode

Enterprises fail to get the most value from threat monitoring, detection, and response cybersecurity strategies because they’re too focused on data collection and security monitoring alone. CISOs tell VentureBeat they’re capturing more telemetry (i.e., remote) data than ever, yet are short-staffed when it comes to deciphering it, which means they’re often in react mode.

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DNA damage is constantly occurring in cells, either due to external sources or as a result of internal cellular metabolic reactions and physiological activities. Accurate repair of such DNA damages is critical to avoid mutations and chromosomal rearrangements linked to diseases including cancer, immunodeficiencies, neurodegeneration, and premature aging.

A team of researchers at Massachusetts General Hospital and the National Cancer Research Centre have identified a way to repair genetic damage and prevent DNA alterations using machine learning techniques.

The researchers state that it is possible to learn more about how cancer develops and how to fight it if we understand how DNA lesions originate and repair. Therefore, they hope that their discovery will help create better cancer treatments while also protecting our healthy cells.

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According to new research by Datagen, 99% of computer vision (CV) teams have had a machine learning (ML) project canceled due to insufficient training data. Delays, meanwhile, appear truly ubiquitous, with 100% of teams reporting experiencing significant project delays due to insufficient training data. The research also indicates that these training data challenges come in many forms and affect CV teams in near-equal measure. The top issues experienced by CV teams include poor annotation (48%), inadequate domain coverage (47%), and simple scarcity (44%).

The scarcity of robust, domain-specific training data is only compounded by the fact that the field of computer vision is lacking many well-defined standards or best practices. When asked how training data is typically gathered at their organizations, respondents revealed a patchwork of sources and methodologies are being employed both across the field and within individual organizations. Whether synthetic or real, collected in-house or sourced from public datasets, organizations appear to be utilizing any and all data they can in order to train their computer vision models.

However, computer vision teams have already identified and begun to embrace synthetic data as a solution. Ninety-six percent of CV teams reported having already adopted the use of synthetic data to help train their AI/ML models. Nevertheless, the quality, source, and proportion of synthetic data that’s used remains highly variable across the field, and only 6% of teams currently use synthetic data exclusively.

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