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Accelerating AI tasks while preserving data security
MIT researchers have developed a search engine, called SecureLoop, that can identify optimal designs for deep neural network accelerators while maintaining data security. The tool considers the impact of adding encryption and authentication measures on performance and energy usage. It improves accelerator designs by boosting performance and keeping data protected, enabling the improvement of AI…
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The brain may learn about the world the same way some computational models do
MIT researchers have found evidence suggesting that the brain may develop an intuitive understanding of the physical world through a process similar to self-supervised learning. Using models known as neural networks, they trained them using self-supervised learning techniques and found that the resulting models generated activity patterns similar to those seen in the brains of…
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Researchers from Columbia University and Apple Introduce Ferret: A Groundbreaking Multimodal Language Model for Advanced Image Understanding and Description
The researchers from Columbia University and Apple have developed Ferret, a multimodal large language model (MLLM) that combines referencing and grounding for improved image understanding and description. Ferret uses a hybrid region representation and a spatial-aware visual sampler to handle a variety of regional forms and can handle input that combines free-form text and referenced…
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UX Conference January Announced (Jan 12 – Jan 26)
AI training courses and a conference focused on UX skills are available from January 12 to January 26, 2024. The courses aim to teach best practices for successful design and provide long-lasting skills for UX professionals. See the full schedule and pricing for more details. Maximum word count: 50.
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Joy Buolamwini: “We’re giving AI companies a free pass”
Joy Buolamwini, a prominent AI researcher and activist, calls for a radical rethink of AI systems, highlighting the unethical practices of many AI companies. She emphasizes the need for rigorous testing and auditing of AI systems before deployment to avoid harmful consequences. Buolamwini also shares her personal journey of becoming an accidental activist and the…
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Microsoft’s first-quarter financial results surpass analyst expectations
Microsoft exceeded Wall Street’s Q1 financial projections across all sectors, driven by cloud computing and the Windows operating system. The company’s revenue also surpassed analysts’ expectations, largely due to the anticipation of the release of Microsoft 365 Copilot, a suite of AI tools developed in collaboration with OpenAI. Azure’s revenue grew by 29%, outperforming projections.…
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OpenAI builds new “Preparedness” team to handle AI’s existential risks
OpenAI has established a team called “Preparedness” to address the potential risks associated with AI. The team will evaluate current and future AI models for risks such as tailored persuasion, cybersecurity threats, autonomous replication, and even existential threats like chemical, biological, and nuclear attacks. OpenAI believes that while advanced AI models can benefit humanity, they…
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I landed my first Data job, what’s next?
The author discusses how to succeed in your first data role. They emphasize the importance of becoming comfortable with workflow and data structure, mastering the company’s toolbox, learning the business, sharpening your skills, and becoming self-sufficient. They suggest practicing unused skills, creating personal projects, and managing projects from start to end. In a year or…
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Meet GROOT: A Robust Imitation Learning Framework for Vision-Based Manipulation with Object-Centric 3D Priors and Adaptive Policy Generalization
GROOT is a new imitation learning technique developed by researchers at The University of Texas at Austin and Sony AI. It addresses the challenge of enabling robots to perform well in real-world settings with changing backgrounds, camera viewpoints, and object instances. GROOT focuses on building object-centric 3D representations and uses a transformer-based strategy to reason…
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MLCommons and Big Tech to develop AI safety benchmarks
MLCommons has formed the AI Safety Working Group (AIS) to develop benchmarks for AI safety. Currently, there is no standardized benchmark to compare the safety of different AI models. AIS will build upon the Holistic Evaluation of Language Models (HELM) framework developed by Stanford University to create safety benchmarks for large language models. Several prominent…