It is important that consumers and regulators can verify the provenance of large neural models to evaluate their capabilities and risks. We introduce the concept of a Proof-of-Training-Data: any protocol that allows a model trainer to convince a Verifier of the training data that produced a set of model weights. Such protocols could verify the amount and kind of data and compute used to train the model, including whether it was trained on specific harmful or beneficial data sources. We explore efficient verification strategies for Proof-of-Training-Data that are compatible with most current large-model training procedures. These include a method for the model-trainer to verifiably pre-commit to a random seed used in training, and a method that exploits models' tendency to temporarily overfit to training data in order to detect whether a given data-point was included in training. We show experimentally that our verification procedures can catch a wide variety of attacks, including all known attacks from the Proof-of-Learning literature.

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According to research from IDC, over 40% of all marketing materials aren't used by sales teams. The primary reason is that sales reps are unable to locate or access the materials when needed. But in top-performing organizations, sales and marketing are closely aligned. A CRM system is usually the starting point, but the technology available to support sales marketing alignment go well beyond a common platform for customer and prospect contact information. The eight tools here deliver a range of functionality such as email automation, automatic email and call logging, workflow scheduling, lead qualification, sales enablement, and personalized direct mail.

Since the 2013 Target breach, it's been clear that companies need to respond better to security alerts even as volumes have gone up. With this year's fast-spreading ransomware attacks and ever-tightening compliance requirements, response must be much faster. Adding staff is tough with the cybersecurity hiring crunch, so companies are turning to machine learning and artificial intelligence (AI) to automate tasks and better detect bad behavior. In a cybersecurity context, AI is software that perceives its environment well enough to identify events and take action against a predefined purpose. AI is particularly good at recognizing patterns and anomalies within them, which makes it an excellent tool to detect threats.

Any AI effort will rely on three main building blocks: data, infrastructure, and talent. The following is a guest post by Rita C. Waite, a Growth Strategy & Investments Manager at Juniper Networks. Artificial Intelligence (AI) is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation. Advancements in AI over the last decade are presenting opportunities for companies to automate business processes, transform customer experiences, and differentiate products offerings. AI pioneers like Google and Amazon, who have adopted these new technologies to create a growing competitive advantage, have already witnessed bottom-line benefits from their AI strategies.

Both artificial intelligence and social media marketing are getting a lot of attention nowadays because of their huge benefits and growth potential. They are benefiting both businesses and normal people in various ways. The investment has already been growing in the artificial intelligence, and the investment is further expected to grow by around 300%, according to the prediction made by the Forrester. Talking about the social media platforms, more than 2.5 billion people are already using various social media platforms as per the statistic. This is nearly a 1/3 population of the whole planet.

Graphic design used to require physical work. To compose letterheads, business cards, brochures, magazines, books, and posters, you hunched over a desk or a light table. You cut and pasted paper or assembled metal type on a printing press. You processed 35mm film by hand, developing pictures in a darkroom with chemicals. Jason Tselentis is an educator, writer, and designer.

Any AI effort will rely on three main building blocks: data, infrastructure, and talent. The following is a guest post by Rita C. Waite, a Growth Strategy & Investments Manager at Juniper Networks. Artificial Intelligence (AI) is fundamentally changing how businesses operate across all sectors, including manufacturing, healthcare, IT, and transportation. Advancements in AI over the last decade are presenting opportunities for companies to automate business processes, transform customer experiences, and differentiate products offerings. AI pioneers like Google and Amazon, who have adopted these new technologies to create a growing competitive advantage, have already witnessed bottom-line benefits from their AI strategies.

The annual Workshop on the Validation and Verification of Knowledge-Based Systems is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). The 1994 workshop was significant in that there was a definitive move in the philosophical position of the workshop from a testing-and toolbased approach to KBS evaluation to that of a formal specification-based approach. This workshop included 12 full papers and 5 short papers and was attended by 35 researchers from government, industry, and academia. The workshop is the leading forum for presenting research on the validation and verification of knowledge-based systems (KBSs). It has influenced the evolution of the discipline from its origins in 1988; at this time, researchers were asking the questions, How can we evaluate the correctness of KBS? How is this process different from conventional system evolution?

In reviewing a book of this kind, it is necessary to answer three questions: (1) how important is the workshop topic, (2) how valuable are the included papers, and (3) how coherent is the volume as a whole? I address each question in turn. In the last decade, knowledgebased systems (KBSs) emerged from being a research subfield within AI to become an application software technology. Although many specific aspects of knowledge acquisition, representation, and reasoning remained active research topics, the methods and tools required to build useful and powerful KBS applications had become sufficiently well understood to facilitate the development and delivery of systems in many diverse domains. However, as organizations began to use the technology, concerns arose about the reliability of KBSs.