Lifeboat Foundation

George Hotz, the 32-year-old CEO of Comma AI who made a name for himself as the hacker “geohot” when he was just a teenager, announced that he is stepping away from his company on his GitHub page. According to Hotz, he no longer feels “capable” to continue leading the driver-assist technology company he created seven years ago.

Hotz has had a long history in the tech industry despite his young age. He gained notoriety in hacker communities at the age of 17 after becoming the first person to carrier unlock the iPhone. He also bumped heads with Sony a few years later for hacking the PlayStation 3.

Hotz also got into a disagreement with Elon Musk in 2015 after Musk allegedly wanted to hire him because he thought he could improve Tesla’s Autopilot software. Hotz later founded Comma AI, which focused itself on driver-assist technologies. In true hacker fashion, Hotz’s autonomous driving code, “openpilot,” was posted online for free.

Research from Imperial College London suggests that gold compouds recovered from discarded SIM cards can significantly lower the cost of making medicines.

Starlink and T-Mobile’s partnership will be revolutionary for cellular service and Smarter AI CEO Chris Piche had some thoughts on how the new partnership will impact 5G capability for the automotive industry.

Chris, who has created services including AT&T TV, BBM Video, Poly Video, and STUN/TURN/ICE shared his thoughts on the effect of 5G on vehicles and telecommunications in an interview with Teslarati.

AI CAMERAS, TESLA, STARLINK & AUTONOMOUS VEHICLES Before founding Smarter AI, the Top 40 under 40 entrepreneur’s company created a technology that BlackBerry licensed to enable voice and video calling. This gave Chris a front-row seat to witness the speed at which technology can transform markets.

Data security — information security newspaper | hacking news.

Nerve cells can regulate their sensitivity to incoming signals autonomously. A new study led by the University of Bonn has now discovered a mechanism that does just that. The German Center for Neurodegenerative Diseases and the Max Planck Institute for Neurobiology of Behavior were involved in the work. The results have now been published in the journal Cell Reports.

Anyone who has ever sent a voice message with a cell phone knows how much the volume matters: Shouting into the microphone results in a distorted and unclear recording. But whispering is not a good idea either—then the result is too quiet and also difficult to understand. That is why sound engineers ensure the perfect sound at every concert and talk show: They regulate each microphone’s gain to match the input signal.

The neurons in the brain can also fine-tune their sensitivity, and even do so autonomously. A new study led by the University of Bonn and the University Hospital Bonn shows how they do this. For this purpose, the participants investigated nerve cell networks that also play a role in vision, hearing and touch. The stimulus first travels to the so-called thalamus, a structure deep in the center of the brain. From there, it is then conducted to the cerebral cortex, where it is further processed.

Researchers in the field of optical spectrometry have created a better instrument for measuring light. This advancement could improve everything from smartphone cameras to environmental monitoring.

The research, led by Finland’s Aalto University, developed a powerful, incredibly small spectrometer that fits on a microchip and is run by artificial intelligence. Their research was recently published in the journal Science.

The study used a relatively new class of super-thin materials known as two-dimensional semiconductors, and the result is a proof of concept for a spectrometer that could be easily integrated into a number of technologies such as quality inspection platforms, security sensors, biomedical analyzers, and space telescopes.

The use of smartphones has become an increasingly popular behaviour in people’s lives. However, an increased number of people find it difficult to minimise the use of smartphones, leading to the emergence of smartphone-addictive behaviours (Panova and Carbonell, 2018; Busch and McCarthy, 2021). In particular, the rapid spread of coronavirus disease 2019 around the world has led to a dramatic increase in the number of smartphone addicts due to home isolation (Caponnetto et al., 2021). Smartphone addiction is an emerging behavioural addiction, which refers to excessive dependence on and abuse of smartphones by individuals (Kwon et al., 2013; Billieux et al., 2015). Notably, smartphone addiction has been reported to have negative impacts on individuals’ cognitive functions, such as attention (Choi et al., 2021; Lee et al., 2021), perception (Dong et al., 2014) and memory (Hartanto and Yang, 2016; Tanil et al., 2020). Nevertheless, the influence of smartphone addiction on individuals’ advanced cognition is still unclear. Smartphone addiction may impair flexible cognitive processes (Dong et al., 2014), such as those that contribute to creative cognition. However, to our knowledge, the influence of smartphone addiction on creative cognition has not been explored.

Given the negative effects and high incidence of smartphone addiction (Zou et al., 2021), it is essential to uncover the underlying mechanisms, especially the neural mechanisms, by which smartphone addiction affects creative cognition. Creative cognition is defined as the ability to generate original and useful products (Sternberg and Lubart, 1999). It is a core cognitive element that allows for daily flexible problem solving and the generation of new ideas. The main components of creative cognition are (i) overcoming the semantic constraints of existing knowledge, which involves goal-directed behaviour through cognitive control, and (ii) building unusual associations to expand the existing structure of knowledge, which involves the spontaneous and unconstrained generation of novel associations (Ward et al., 1997; Abraham, 2014; Marron and Faust, 2019).

According to the problematic mobile phone use model (Billieux et al., 2015), the lack of planning or reduced cognitive control is a crucial contributor to smartphone addiction behaviour. Previous studies have also indicated that impaired cognitive control is a prominent feature of smartphone addicts, characterised by an inability to focus on task-related information and an inability to suppress dominant, automatic responses (Van Deursen et al., 2015; Li et al., 2021). In fact, previous studies have emphasised the contribution of cognitive control to the generation of creative ideas (Beaty et al., 2016; Benedek and Fink, 2019). During creative idea generation, known ideas are often initially retrieved, which acts as a source of interference allowing the retrieval process to focus on familiar and dominant ideas (Abraham, 2014). In this context, cognitive control is needed to drive the retrieval process of novel and remote information.

Computer scientists at New York University and Michigan State University have trained an artificial neural network to create fake digital fingerprints that can bypass locks on cell phones. The fakes are called “DeepMasterPrints”, and they present a significant security flaw for any device relying on this type of biometric data authentication. After exploiting the weaknesses inherent in the ergonomic needs of cellular devices, DeepMasterPrints were able to imitate over 70% of the fingerprints in a testing database.

An artificial neural network is a type of artificial intelligence comprising computer algorithms modeled after the human brain’s ability to recognize patterns. The DeepMasterPrints system was trained to analyze sets of fingerprint images and generate a new image based on the features that occurred most frequently. This “skeleton key” could then be used to exploit the way cell phones authenticate user fingerprints.

In cell phones, the necessarily small size of fingerprint readers creates a weakness in the way they verify a print. In general, phone sensors only capture a partial image of a print when a user is attempting to unlock the device, and that piece is then compared to the phone’s authorized print image database. Since a partial print means there are fewer characteristics to distinguish it than a full print, a DeepMasterPrint needs to match fewer features to imitate a fingerprint. It’s worth noting that the concept of exploiting this flaw is not unique to this particular study; however, generating unique images rather than using actual or synthesized images is a new development.

In recent years, many smartphone users have become concerned about the privacy of their data and the extent to which companies might have access to this data. As things stand today, the applications that users can run on their phone and what they can do with these applications is determined by a few big tech companies.

Researchers at ETH Zurich have recently set out on a quest to change this current trend, through the development of a new smartphone architecture called TEEtime. This architecture, introduced in a paper pre-published on arXiv, allows users to flexibly choose what resources on their smartphone they will dedicate to legacy operating systems, such as Android or iOS, and which they wish to keep for their own proprietary software and data.

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