Many data scientists become disillusioned when they are hired for statistics and machine learning, but instead find themselves being the resident “IT expert” instead. This phenomena is not new and actually predates data science. Shadow information technology (shadow IT) describes office workers who create systems outside their IT department. This includes databases, dashboards, scripts, and code. This used to be frowned on in organizations, as it is unregulated and operating outside the IT department’s scope of control. However, one benefit of the data science movement is it has made shadow IT more accepted as a necessity for innovation. Rather than be disillusioned, a data scientist can gain proficiency in SQL, programming, cloud platforms, web development, and other useful technologies. After all, a data scientist works with data and that implicitly can lead to IT-work. It can also make their work streamlined and more accessible to others, and open up possibilities for statistical and machine learning models.
The research found that, for many organizations, cybersecurity is not a major design factor; only 51% build cybersecurity practices in their smart factories by default. Unlike IT platforms, all organizations may not be able to scan machines at a smart factory during operational uptime. System-level visibility of IIoT and OT devices is essential to detect when they have been compromised; 77% are concerned about the regular use of non-standard smart factory processes to repair or update OT/IIOT systems. This challenge partly originates from the low availability of the correct tools and processes, however 51% of organizations, said that smart factory cyberthreats primarily originate from their partner and vendor networks. Since 2019, 28% noted a 20% increase in employees or vendors bringing in infected devices, such as laptops and handheld devices, to install/patch smart-factory machinery. ... When it comes to incidents, only a few of the organizations surveyed claimed that their cybersecurity teams have the required knowledge and skills to carry out urgent security patching without external support.
The human brain is hardwired to infer intentions behind words. Every time you engage in conversation, your mind automatically constructs a mental model of your conversation partner. You then use the words they say to fill in the model with that person’s goals, feelings and beliefs. The process of jumping from words to the mental model is seamless, getting triggered every time you receive a fully fledged sentence. This cognitive process saves you a lot of time and effort in everyday life, greatly facilitating your social interactions. However, in the case of AI systems, it misfires – building a mental model out of thin air. A little more probing can reveal the severity of this misfire. Consider the following prompt: “Peanut butter and feathers taste great together because___”. GPT-3 continued: “Peanut butter and feathers taste great together because they both have a nutty flavor. Peanut butter is also smooth and creamy, which helps to offset the feather’s texture.” The text in this case is as fluent as our example with pineapples, but this time the model is saying something decidedly less sensible.
Enterprises have evolved their cloud strategies to multicloud environments and are adopting more containers, microservices and cloud-native technologies. This is creating increasingly distributed systems, making it harder to gain a comprehensive view into how they’re performing, Weiss said. As a result, legacy monitoring tools are obsolete for modern applications. “The reason for that is the change to cloud computing multi-services. Together with the amount of data that is being generated in these applications, you can’t cope with it anymore,” Weiss said. Monitoring merely collects data from the system and alerts admins to something being wrong. Observability goes beyond monitoring to interpret the data, providing answers on why something is wrong and how to fix it, allowing teams to pinpoint the root cause, minimize downtime and increase operational efficiency. “Previously, the solution was to put an agent on the server that can do everything, collect everything – but there is no place to put the agent anymore,” Weiss told VentureBeat. “Services are becoming very volatile. They’re disappearing. They’re here now, they’re not here tomorrow. I’m not even talking about serverless. So, that’s a change that is trending.”
The concept might sound interesting, but the actual application was dodgy. As investigations later showed, almost half of the alleged perpetrators on the list had never been charged for illegal possession of arms, while others had not been charged with serious offenses before. A Technology Review report in 2019 detailed how risk assessment algorithms that determined whether an individual should be sent to jail or not were trained on historically biased data. So, when researchers at the University of Chicago, led by assistant professor Ishanu Chattopadhyay, tried to build their algorithm, they wanted to avoid past mistakes. The algorithm divides a city into 1,000 square feet tiles and uses the historical data on violent and property crimes to predict future events. The researchers told Bloomberg that their model is different from other such algorithmic predictions since the other look at crime as emerging from hotspots and spreading to other areas. However, such approaches, the researchers argue, miss the complex social environment of cities and are also biased by the surveillance used by the state for law enforcement.
SingleStore has also enhanced SingleStoreDB with the addition of Code Engine with Wasm. Now users can bring external data and compute algorithms to power new real-time use cases within the database engine, drawing on WebAssembly. With Code Engine with Wasm, developers can securely, natively, and efficiently execute rich computation in the database using their programming language of choice. For computations and algorithms that are not easily expressed in SQL, Wasm support in SingleStoreDB brings algorithms to the data without having to move that data outside of the database. With SingleStoreDB Universal Language support, enterprises can now quickly integrate machine learning into real-time applications and dashboards. ... The latest release of SingleStoreDB also includes Data API, enabling seamless integrations with applications. Developers can use Data API to build serverless applications including web and mobile apps. Data API uses HTTP to run SQL operations against the database rather than maintaining a persistent TCP connection. The connection is dynamically reconfigured, and each request-response is its own connection.
A novel methodology developed by MIT and Microsoft researchers identifies instances in which autonomous systems have “learned” from training samples that don’t reflect what happens in the real world. Engineers may employ this idea to improve the security of robots and autonomous vehicles that use artificial intelligence. For instance, to prepare them for nearly every eventuality on the road, the artificial intelligence (AI) systems that drive autonomous cars go through extensive training in virtual simulations. But occasionally the car makes an unforeseen error as a result of a situation that ought to alter the way it acts but doesn’t. Consider an autonomous car without the necessary sensors, which would be unable to discern between drastically different conditions like large, white cars and ambulances with red, flashing lights on the road. A driver may not know to slow down and pull over when an ambulance starts its sirens as it is traveling down the highway because it cannot tell the ambulance from a huge white sedan. Like with conventional methods, the researchers trained an AI system using simulations.
While blockchain’s elevator pitch is heavily inclined toward immutability, the technology boasts multiple advantages over traditional software and paper-based systems. The opinions regarding the benefits of blockchain boil down to the control over personal information. Self-sovereignty stands as one of the biggest benefits of blockchain-based digital IDs, according to Martis. This means that blockchain empowers users to share partial or selective information with their service providers instead of handing over their complete identity. With blockchain-based IDs eradicating the misuse of information, experts envision the birth of a truly trustless system without the involvement of third parties. Gentry, too, reiterated verifiability, traceability and uniqueness as some of the major benefits brought about by blockchain, as she highlighted that blockchain IDs cannot be duplicated because it's on the distributed ledger. “All the Digital ID can be verified on the blockchain and can be traced back to the owners' account which can also be used for Know Your Customer,” she added.
“There are not enough skilled people in this field, but neurodivergent individuals bring an essential skillset to cybersecurity -- hyper focus on analyzing data and identifying trends,” explains Rex Johnson, executive director of cybersecurity at CAI. “Not everyone has this ability, or at least do it well, except for neurodiverse talent.” To reach out to neurodiverse professionals, Johnson says organizations must look beyond traditional recruiting methods. “Depending on the need, consider a team of neurodivergent individuals who work under a supervisor who understands how to manage this dynamic and be the liaison to other management teams,” he advises. They can look for organizations that implement an end-to-end neurodiversity employment program that not only bring the right neurodivergent teammate in the door, but also work with the employer to create workplace accommodations that increase retention, morale, and productivity. “Not everyone is the same. People are inspired and motivated by many different visions and missions,” Johnson adds.
Most would not like to admit it, but vulnerabilities are inevitable. Although a ransomware event is likely to affect an organisation at some point, ransomware itself is not completely out of the control of a business. Vendors have an ethical imperative to be transparent with the customer community when they become aware of a vulnerability in their product, providing clear assessment of impact and steps to remediate. As soon as any vulnerability in its software is known, speed and effectiveness in sharing relevant information and patches with customers and stakeholders are crucial. Once alerted, the impacted customer community then has a shared responsibility to action this information, in the context of the impact on their business and what that means for their resilience and continuity of operations. Here the vendor’s responsibility clearly becomes double-edged. Vendors must be transparent so their customers can apply the fix, yet this sets off a ticking time bomb as threat actors continuously scour the internet for this type of information, hoping to exploit the vulnerability before organisations have had time to apply the patch.
Quote for the day:
"People seldom improve when they have no other model but themselves." -- Oliver Goldsmith