Open source software offers greater transparency to the teams that use it; visibility into both the code itself and how it is maintained. Giving organizations access to the source code allows them the opportunity to evaluate the security of the code for themselves. Additionally, users have more visibility into how and what changes are made to the code base, including the pre-release review process, how often dependencies are updated and how developers and organizations respond to security vulnerabilities. As a result, open source software users have a more complete picture of the overall security of the software they’re using. Another major benefit is found in the communities which drive the growth and development of open source software. The vast majority of open source software is backed by communities of forward-thinking developers, many of whom use the same software they build and maintain as a primary means of communicating with team members. Open source developers and the communities around the software value users’ input to a significant degree, and many user suggestions end up getting incorporated into new versions.
A.I. is being used to analyze vast amounts of space data and is having an enormous impact on health care. A.I. image and scan analysis are, for example, helping doctors identify breast and colon cancer. It’s also showing potential in vaccine creation. I guarantee that A.I. will someday save lives. It’s those kinds of A.I.-driven data analysis that gets shoved aside by news of an A.I. beating a world-champion GO player or the world’s best-known entrepreneur raising alarms about a situation where “A.I. is vastly smarter than humans.” That kind of fear-mongering leads consumers, who don’t understand the differences between A.I. that scans a crowd of 10,000 faces for one suspect and one that can create recipes based on pleasing ingredient combinations, to mistrust all A.I., and to write the kind of stifling regulation produced by the EU. Even if you still think the negatives outweigh the benefits, we’ll arguably need better and bigger A.I. to manage and sift through the mountains of data we produce every single day. To deny A.I.’s role in this is like saying we don’t need garbage collection services and that our debris can just pile up on street corners indefinitely.
AutoNLP is a tool to automate the process of creating end-to-end NLP models. AutoNLP is a tool developed by the Hugging Face team which was launched in its beta phase in March 2021. AutoNLP aims to automate each phase that makes up the life cycle of an NLP model, from training and optimizing the model to deploying it. “AutoNLP is an automatic way to train and deploy state-of-the-art NLP models, seamlessly integrated with the Hugging Face ecosystem.” — AutoNLP team One of the great virtues of AutoNLP is that it implements state-of-the-art models for the tasks of binary classification, multi-class classification, and entity recognition, supported in 8 languages which are: English, German, French, Spanish, Finnish, Swedish, Hindi, and Dutch. Likewise, AutoNLP takes care of the optimization and fine-tuning of the models. In the security and privacy part, AutoNLP implements data transfers protected under SSL, also the data is private to each user account. As we can see, AutoNLP emerges as a tool that facilitates and speeds up the process of creating NLP models. In the next section, will see how the experience was like from start to finish when creating a text classification model using AutoNLP.
Even if the array of technologies offered brilliant solutions, it would be difficult for them to mimic empathy. Why? Because at the core of compassion, there is the process of building trust: listening to the other person, paying attention to their needs, expressing the feeling of understanding and responding in a manner that the other person knows they were understood. At present, you would not trust a robot or a smart algorithm with a life-altering decision; or even with a decision whether or not to take painkillers, for that matter. We don’t even trust machines in tasks where they are better than humans – like taking blood samples. We will need doctors holding our hands while telling us about a life-changing diagnosis, their guide through therapy and their overall support. An algorithm cannot replace that. ... More and more sophisticated digital health solutions will require qualified medical professionals’ competence, no matter whether it’s about robotics or A.I. The human brain is so complex and able to oversee such a vast scale of knowledge and data that it merely is not worth developing an A.I. that takes over this job – the human brain does it so well. It is more worthwhile to program those repetitive, data-based tasks, and leave the complex analysis/decision to the person.
Machines have been trying to mimic the human brain for decades. But neither the original, symbolic AI that dominated machine learning research until the late 1980s nor its younger cousin, deep learning, have been able to fully simulate the intelligence it’s capable of. One promising approach towards this more general AI is in combining neural networks with symbolic AI. In our paper “Robust High-dimensional Memory-augmented Neural Networks” published in Nature Communications, we present a new idea linked to neuro-symbolic AI, based on vector-symbolic architectures. We’ve relied on the brain’s high-dimensional circuits and the unique mathematical properties of high-dimensional spaces. Specifically, we wanted to combine the learning representations that neural networks create with the compositionality of symbol-like entities, represented by high-dimensional and distributed vectors. The idea is to guide a neural network to represent unrelated objects with dissimilar high-dimensional vectors. In the paper, we show that a deep convolutional neural network used for image classification can learn from its own mistakes to operate with the high-dimensional computing paradigm, using vector-symbolic architectures.
The Manufacturing Data Excellence Framework, developed by a community of companies hosted by the World Economic Forum’s Platform for Shaping the Future of Advanced Manufacturing and Production, serves this purpose. We introduced this framework, comprising 20 different dimensions with five different maturity levels, in our recent white paper, “Data Excellence: Transforming manufacturing and supply systems”. “One of the challenges we face when discussing the industry transformation towards data ecosystems is the lack of commonality of terminology. It’s very powerful to have a tool in which we have created common definitions and explanations, and around which we can build the foundations towards data sharing excellence in manufacturing,” says Niall Murphy, CEO and Co-founder of EVRYTHNG. The first step is an assessment of the status quo using the framework. Companies will be able to objectively assess their maturity in implementing applications and technological and organizational enablers. They will then be able to compare their individual maturity versus the benchmark and define their individual target state.
Ethical issues take on greater resonance when AI expands to uses that are far afield of the original academic development of algorithms. The industrialization of the technology is amplifying the everyday use of those algorithms. A report this month by Ryan Mac and colleagues at BuzzFeed found that "more than 7,000 individuals from nearly 2,000 public agencies nationwide have used technology from startup Clearview AI to search through millions of Americans' faces, looking for people, including Black Lives Matter protesters, Capitol insurrectionists, petty criminals, and their own friends and family members." Clearview neither confirmed nor denied BuzzFeed's' findings. New devices are being put into the world that rely on machine learning forms of AI in one way or another. For example, so-called autonomous trucking is coming to highways, where a "Level 4 ADAS" tractor trailer is supposed to be able to move at highway speed on certain designated routes without a human driver. A company making that technology, TuSimple, of San Diego, California, is going public on Nasdaq. In its IPO prospectus, the company says it has 5,700 reservations so far in the four months since it announced availability of its autonomous driving software for the rigs.
Fundamentally, it’s more economic to do the right thing than the wrong thing. Renewable energy, for example, is a great democratizing force in world affairs because the wind and the sun are available to every country on the planet, whereas oil and gas are not. We fight wars over oil and gas quite literally because it’s such a precious resource. And here in Britain, we spend £55 billion [US$76 billion] every year buying fossil fuels from abroad to bring them here to burn them. And if we spent that money on wind and solar machines instead, we could make our own electricity, create jobs, and be independent from fluctuating global fossil fuel markets and currency exchanges. We can create a stronger, more resilient economy, as well as a cleaner one. ... I think businesses historically reinvent themselves. They move with the times or they die, and that’s a natural order of things. And some businesses just get left behind because their business model becomes outdated. A nimble, adaptive business will move from the old way of doing things and will still be here.
ERISA’s duty of prudence requires fiduciaries to act “with the care, skill, prudence, and diligence under the circumstances then prevailing that a prudent man acting in a like capacity and familiar with such matters would use in the conduct of an enterprise of a like character and with like aims.” It has become generally accepted that ERISA fiduciaries have some responsibility to mitigate the plan’s exposure to cybersecurity events. But, prior to this guidance, it was not clear what the DOL considered to be prudent with respect to addressing cybersecurity risks associated, including those related to identity theft and fraudulent withdrawals. Each of the three new pieces of guidance addresses a different audience. The first, Tips for Hiring a Service Provider with Strong Cybersecurity Practices (Tips for Hiring a Service Provider), provides guidance for plan fiduciaries when hiring a service provider, such as a recordkeeper, trustee, or other provider that has access to a plan’s nonpublic information. The second, Cybersecurity Program Best Practices (Cybersecurity Best Practices), is, as the name indicates, a collection of best practices for recordkeepers and other service providers, and may be viewed as a reference for plan fiduciaries when evaluating service providers’ cybersecurity practices. The third, Online Security Tips (Online Security Tips), contains online security advice for plan participants and beneficiaries. We have summarized each piece of guidance below along with our key observations.
The panellists agreed that it means going back to layer by layer design principles with clean APIs up and down the protocol stack from application to the lowest levels of connectivity. Without such design rigour, programming or operator errors in a complex highly distributed system could have profound consequences. Cisco’s Pandey says that while it appeared “horribly scary” in terms of connectivity to take monolithic apps and make them cloud-native, the upside is that the resulting discrete components of the application can be swapped out or taken down with fewer consequences to the rest of the system and ultimately to customers. But, he warned, “you need to have the tools and capabilities to monitor it – the full-stack observability piece. You need to have discoverability and you need to have security at the API layer all the way down so that you can manage things properly”. His comments were echoed by Alkira’s Khan, who pointed out that the problems of a distributed architecture are particularly acute for enterprises trying to apply a security posture in a multi-cloud environment.
Quote for the day:
"It is the responsibility of leadership to provide opportunity, and the responsibility of individuals to contribute." - William Pollard