A different approach to creating AI, proposed by the DeepMind researchers, is to recreate the simple yet effective rule that has given rise to natural intelligence. “[We] consider an alternative hypothesis: that the generic objective of maximising reward is enough to drive behaviour that exhibits most if not all abilities that are studied in natural and artificial intelligence,” the researchers write. This is basically how nature works. As far as science is concerned, there has been no top-down intelligent design in the complex organisms that we see around us. Billions of years of natural selection and random variation have filtered lifeforms for their fitness to survive and reproduce. Living beings that were better equipped to handle the challenges and situations in their environments managed to survive and reproduce. The rest were eliminated. This simple yet efficient mechanism has led to the evolution of living beings with all kinds of skills and abilities to perceive, navigate, modify their environments, and communicate among themselves.
New roles such as machine learning architecture are being created today. As the platform gets bigger, the machine learning engineer should handle the entire architecture and evolve to meet the needs of the data science, machine learning and data analytics organisations. The most important aspect of an ML engineer is the focus on production and model deployment — not just code that works, but code that functions in the real world, alongside understanding industry best practices to successfully integrate and deploy machine learning models. For starters, having a computer science, robotics, engineering and physics degree, along with competencies in C, C++, Java, Python, R, Scala, Julia, and other enterprise languages, helps. Plus, a stronger understanding of databases adds weightage. At experience levels, software engineers, software developers, and software architects are cut out for machine learning engineering roles. “It is almost a straight line from cloud architect to ML engineer and ML architect, as these two roles have so much overlap. If you understand data science and machine learning, you can understand models,” said Vashishta.
Microsoft's .NET development framework ranked high in recent research about coding bootcamps, or "immersive technology education." With an average cost of about $14,000, these accelerated learning programs can last from six to 28 weeks -- averaging about 14 -- and promise to advance careers in both technical chops and bottom-line salary increases. Course Report studies the industry and presents its findings in annual reports that can help coders pick the best option, among some 500 around the world, with the choice of programming language being a primary factor. "Coding bootcamps employ teaching languages to introduce students to the world of programming," Course Report said in its latest study: Coding Bootcamps in 2021, an update of a 2020 report. "While language shouldn't be the main deciding factor when choosing a bootcamp, students may have specific career goals that guide them towards a particular language. In that case, first decide whether you'd prefer to learn web or mobile development. For the web, your main choices are Ruby, Python, LAMP stack, MEAN stack and .NET languages.
Amazon Sidewalk will create a mesh network between smart devices that are located near one another in a neighborhood. Through the network, if, for instance, a home WiFi network shuts down, the Amazon smart devices connected to that home network will still be able to function, as they will be borrowing internet connectivity from neighboring products. Data transfer between homes will be capped, and the data communicated through Amazon Sidewalk will be encrypted. Amazon smart device owners will automatically be enrolled into Amazon Sidewalk, but they can opt out before a June 8 deadline. That deadline has irked many cybersecurity and digital rights experts, as Amazon Sidewalk itself was not unveiled until June 1—just one week before a mass rollout. Jon Callas, director of technology projects at Electronic Frontier Foundation, told the news outlet ThreatPost that he did not even know about Amazon’s white paper on the privacy and security protocols of Sidewalk until a reporter emailed him about it. “They dropped this on us,” Callas said in speaking to ThreatPost. “They gave us seven days to opt out.”
Recent studies suggest that new brain cells are being formed every day in response to injury, physical exercise, and mental stimulation. Glial cells, and in particular the ones called oligodendrocyte progenitors, are highly responsive to external signals and injuries. They can detect changes in the nervous system and form new myelin, which wraps around nerves and provides metabolic support and accurate transmission of electrical signals. As we age, however, less myelin is formed in response to external signals, and this progressive decline has been linked to the age-related cognitive and motor deficits detected in older people in the general population. Impaired myelin formation also has been reported in older individuals with neurodegenerative diseases such as Multiple Sclerosis or Alzheimer’s and identified as one of the causes of their progressive clinical deterioration. ... The discovery also could have important implications for molecular rejuvenation of aging brains in healthy individuals, said the researchers. Future studies aimed at increasing TET1 levels in older mice are underway to define whether the molecule could rescue new myelin formation and favor proper neuro-glial communication.
Jake Moore, cybersecurity specialist at internet security firm ESET, said refusing to pay a ransom is “not a decision to be taken lightly.” Ransomware gangs often threaten to leak or sell sensitive data if payment is not made. However, Fujifilm Europe said it is “highly confident that no loss, destruction, alteration, unauthorised use or disclosure of our data, or our customers’ data, on Fujifilm Europe’s systems has been detected.” The spokesperson added: “From a European perspective, we have determined that there is no related risk to our network, servers and equipment in the EMEA region or that of our customers across EMEA. We presently have no indication that any of our regional systems have been compromised, including those involving customer data.” It is not clear if the ransomware gang stole Fujifilm data from the affected network in Japan. Fujifilm declined to comment when asked if those responsible had threatened to publish data if the ransom is not paid. According to security news site Bleeping Computer, Fujifilm was infected with the Qbot trojan last month.
The challenge is that one party, the developers, has more information than other parties. That information asymmetry is what creates unbalanced risk sharing. Coping with information asymmetry has led to all kinds of new collaborative models, starting with DevOps and evolving into DevSecOps and other permutations like BizDevSecOps. True collaboration has been hard to come by. Early DevOps efforts are often successful, but scaling beyond five to seven teams is difficult because teams lack the breadth of experience in IT operations or the SRE capacity to staff multiple product teams. The change velocity DevOps teams can achieve is often far greater than SREs and SecOps can absorb, making information asymmetry worse. If teams can’t maintain high levels of collaboration and communication, another option must be developed. Observability practices, like collecting all events, metrics, traces and logs, allow SREs and SecOps teams to interrogate applications about their behavior without knowing which questions they want to ask ahead of time. However, observability only works if applications, and the infrastructure they rely on, are instrumented.
The first role on a project team and arguably the most important role on a product team is your executive steering committee. This is typically a cross-functional group of executives within your organization that are responsible for setting the vision for the overall transformation. They’re responsible for approving scope changes, or any sort of material changes to the project plan or the budget. They’re ultimately responsible for setting the tone and the vision for the overall future state. The question to ponder on here is, how do we want our operation model to look and what do we want our organization to look like in the future? For lack of a better word, what do we want to be when we grow up? Before I get into the rest of the project team, something that’s very important even before we talk about other team roles is who should fill these other roles. The first thing you want to do is make sure that the steering committee is aligned on the overall transformation, vision, strategy, and objectives. If you start filling out the project team prematurely, when you don’t have that alignment
After it compromises web servers, Siloscape uses container escape tactics to achieve code execution on the Kubernetes node. Prizmant said that Siloscape’s heavy use of obfuscation made it a chore to reverse-engineer. “There are almost no readable strings in the entire binary. While the obfuscation logic itself isn’t complicated, it made reversing this binary frustrating,” he explained. The malware obfuscates functions and module names – including simple APIs – and only deobfuscates them at runtime. Instead of just calling the functions, Siloscape “made the effort to use the Native API (NTAPI) version of the same function,” he said. “The end result is malware that is very difficult to detect with static analysis tools and frustrating to reverse engineer.” “Siloscape is being compiled uniquely for each new attack, using a unique pair of keys,” Prizmant continued. “The hardcoded key makes each binary a little bit different than the rest, which explains why I couldn’t find its hash anywhere. It also makes it impossible to detect Siloscape by hash alone.”
Whether it's stand-alone IoT sensors, devices of all kinds, drones, or autonomous vehicles, there's one thing in common. Increasingly, data generated at the edge are used to feed applications powered by machine learning models. There's just one problem: machine learning models were never designed to be deployed at the edge. Not until now, at least. Enter TinyML. Tiny machine learning (TinyML) is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices. ... First, the working definition of what constitutes TinyML was, and to some extent still is, debated. What matters is how devices can be deployed in the field and how they're going to perform, said Gousev. That will be different depending on the device and the use case, but the point is being always on and not having to change batteries every week. That can only happen in the mW range and below.
Quote for the day:
"If you're relying on luck, you have already given up." -- Gordon Tredgold