Generalized self-concordance is a key property present in the objective function of many important learning problems. We establish the convergence rate of a simple Frank-Wolfe variant that uses the open-loop step size strategy $\gamma_t = 2/(t+2)$, obtaining a $\mathcal{O}(1/t)$ convergence rate for this class of functions in terms of primal gap and Frank-Wolfe gap, where $t$ is the iteration count. This avoids the use of second-order information or the need to estimate local smoothness parameters of previous work. We also show improved convergence rates for various common cases, e.g., when the feasible region under consideration is uniformly convex or polyhedral.

Tech expert Kurt Knutsson says Hugging Face has launched affordable open-source humanoid robots for home AI exploration. Cable bills are only getting higher, and many people are looking for ways to save without losing access to their favorite shows. "Can you give me any tips on how to cut the cord? I have Verizon Fios right now. I am a senior on a fixed income."

Generalized self-concordance is a key property present in the objective function of many important learning problems. We establish the convergence rate of a simple Frank-Wolfe variant that uses the open-loop step size strategy \gamma_t 2/(t 2), obtaining a \mathcal{O}(1/t) convergence rate for this class of functions in terms of primal gap and Frank-Wolfe gap, where t is the iteration count. This avoids the use of second-order information or the need to estimate local smoothness parameters of previous work. We also show improved convergence rates for various common cases, e.g., when the feasible region under consideration is uniformly convex or polyhedral.

Artificial intelligence (AI) is on everyone’s mind: autopilot for Teslas, deepfakes for fun or fake news, DALL·E 2 for the untalented artist, big data and machine learning for data analysis. And CERN is no exception. While all those AI applications come with pros and cons, with benefits and risks, AI systems deserve their own consideration when it comes to computer security. So we took the opportunity to ask a renowned expert in AI, ChatGPT, how best to secure AI systems. Here’s the answer we got: “Artificial intelligence (AI) is a rapidly advancing technology that has the potential to greatly benefit society in a variety of ways. However, it also poses new security risks that must be addressed. Here are some best practices for ensuring the security of AI systems: Secure data inputs and outputs. AI systems rely on large amounts of data to learn and make decisions. This data must be protected from unauthorised access and tampering to ensure the integrity and accuracy of the system. Use secure algorithms and protocols. AI systems use complex algorithms and protocols to process data and make decisions. It is important to use secure algorithms that have been thoroughly vetted and tested, and to ensure that all communication between the AI system and other systems is encrypted. Monitor and audit AI systems. Regular monitoring and auditing of AI systems can help detect and prevent security breaches. This includes monitoring for unusual or suspicious behavior, as well as auditing the system’s log files for any signs of unauthorised access or tampering. Implement access controls. Access controls can help prevent unauthorised access to AI systems and the data they process. This includes setting up user accounts and assigning roles and permissions, as well as implementing authentication mechanisms such as biometrics or multi-factor authentication. Stay informed and educated. As AI technology continues to evolve, it is important to stay informed about the latest security risks and best practices. This includes attending relevant conferences and workshops, reading industry publications and staying up-to-date with software updates and patches. Be prepared for the worst. AI systems can be vulnerable to cyberattacks, and it is important to have a plan in place to respond to a security incident. This includes having a trained incident response team, and regularly testing incident response plans to ensure they are effective. By following these best practices, organisations can help ensure the security of their AI systems and protect themselves from potential cyber threats. As AI is permeating all industries, it is crucial for everyone to be aware of the security risks and best practices to mitigate them.” Thanks a lot, ChatGPT bot. So far, nothing special here for AI – it’s a matter of applying the same general security paradigms that every computer user and every service manager should apply to any well-managed computing service (like those run by CERN IT) and individual computing device (like your smartphone or laptop): Make sure that you only input valid and trusted data, URLs, etc. Use encryption to protect your privacy Have intrusion detection and/or an anti-malware program running in the background Deploy access control and opt into 2FA security Train yourself and have auto-update enabled Have an incident response plan. Six simple steps to make your computing service more secure, and to protect your personal assets stored on your laptop and smartphone. Six simple steps, common sense for security-aware humans. Six simple steps, apparently also rather logical for an AI bot. So, tell us: how human is AI; how AI are you? ______ Do you want to learn more about computer security incidents and issues at CERN? Follow our Monthly Report. For further information, questions or help, check our website or contact us at Computer.Security@cern.ch.

In this article, I will talk about Artificial Intelligence again. These days I'm super excited to explore AI, especially AI to produce art. I you think that those arts are created by people, you are wrong, those are created by computers through Artificial Intelligence. Not completely wrong, though, that AI is created by people. One of the most well-known and capable AI tools for producing art is midjourney.

Video Understanding, automates a wide range of business use cases, from retail to health care to agriculture, it enables computers to identify behaviors, objects, and activities in images. In its latest release, Lightning Flash provides support for Video Understanding using Facebook AI Research's new PyTorchVideo library powered by Lightning. Flash is a library for fast prototyping, baselining, and fine-tuning scalable Deep Learning tasks. Using Flash for Video Understanding enables you to train, finetune and infer PyTorch Video models on your own data without being overwhelmed by all the details. Once you get a baseline model you can then seamlessly override the default configurations and experiment with the full flexibility of PyTorch Lightning to get state-of-the-art results on your dataset.

In this "No Fluff" 20 Module Straight To The Point 1 Hour Course, You’ll Find Out How To Get The Most of Your Animated Video Creating Software.

No organization can afford to sit back and ignore the potential of AI. Yet, rushing to adopt AI just because the technology exists can be just as damaging to a business. To fully realize the potential of AI – indeed, any new technology – you need to approach it with a clear business goal or need in mind. In other words, you need to identify and narrow down to the most valuable AI opportunities for your business (which means your approach could be quite different from that of your competitors). Let's explore each step in a little more detail.

No organization can afford to sit back and ignore the potential of AI. Yet, rushing to adopt AI just because the technology exists can be just as damaging to a business. To fully realize the potential of AI – indeed, any new technology – you need to approach it with a clear business goal or need in mind. In other words, you need to identify and narrow down to the most valuable AI opportunities for your business (which means your approach could be quite different from that of your competitors). Let's explore each step in a little more detail.

Creating a great machine learning system is an art. There are a lot of things to consider while building a great machine learning system. But often it happens that we as data scientists only worry about certain parts of the project. But do we ever think about how we will deploy our models once we have them? I have seen a lot of ML projects, and a lot of them are doomed to fail as they don't have a set plan for production from the onset.