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Fine-tuning for GPT-3.5 Turbo is now available

OpenAI has introduced fine-tuning for GPT-3.5 Turbo, and it's planned for GPT-4 as well. This means developers can now customize these models to work better for their specific needs and deploy these tailored models on a larger scale. Early tests reveal that a fine-tuned GPT-3.5 Turbo can even match or surpass the capabilities of the base GPT-4 for certain specific tasks. Importantly, any data used in the fine-tuning process remains the property of the customer and is not utilized by OpenAI or others for training other models.

Meta introduces multimodal AI Model for speech and text

We are a step closer to a universal translator. Meta just announced SeamlessM4T, a all-in-one multilingual multimodal AI translation and transcription model. This single model can perform speech-to-text, speech-to-speech, text-to-speech, and text-to-text translations for up to 100 languages depending on the task.

Meta is publicly releasing SeamlessM4T under a research license to allow researchers and developers to build on this work. They are also releasing the metadata of SeamlessAlign, the biggest open multimodal translation dataset to date, totaling 270,000 hours of mined speech and text alignments.

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AI ‘off-switch’ incoming to Facebook, Instagram in Europe

Meta is set to introduce non-personalized content feeds for users in the European Union (EU) by August 25, in line with the EU's Digital Services Act (DSA). This move comes after a similar step taken by TikTok. The DSA mandates larger platforms to allow users the option to disable AI-driven "personalization," which selects content based on individual tracking and profiling. Larger platforms must offer a choice of non-algorithmic feeds, where content is sorted without using tracking data. This addresses concerns about user autonomy and avoiding filter bubbles.

Meta says it is focusing on transparency measures and claims to offer unprecedented insights into how its AI systems rank content. It aims to maintain user engagement while adhering to the DSA, which limits profiling-based ad targeting and enhances content transparency. Despite these efforts, Meta's compliance challenges and the scale of its task to align with EU regulations remain significant.

Nvidia shares hit all-time high

A "pick-and-shovel play" refers to an investment strategy where investors buy shares of companies that provide tools or services used by an industry to produce goods. This strategy is an indirect approach aiming to profit from the support industries of a booming sector. The term originates from the historical analogy of the Gold Rush era, where investors chose to invest in providers of picks and shovels to gold miners instead of directly mining for gold themselves.

Nvidia's shares have surged to an all-time high on the back of anticipation surrounding the chip designer's upcoming quarterly results. As the key beneficiary of the artificial intelligence (AI) boom, Nvidia's stock has risen by about 19% from a recent two-month low, driven by optimistic expectations that its revenue performance will exceed Wall Street estimates once again. The stock hit a record high of $481.87 before settling at $471.55, with analysts projecting around 110% growth in third-quarter revenue to $12.50 billion when Nvidia reports its results.

At least 19 brokerages have raised their target price on Nvidia, with a median target of $500, representing a 6.5% increase from the stock's last closing price. This year, Nvidia's shares have more than tripled in value, and its performance remains a driving force behind market sentiment and investor confidence.

White House science adviser calls for more safeguards against artificial intelligence

Arati Prabhakar, President Joe Biden's science adviser and director of the White House Office of Science and Technology Policy, is a key figure shaping the U.S. approach to AI technology safeguarding.

Prabhakar discusses the challenges of making AI models explainable, acknowledging the opacity of deep-learning and machine-learning systems. Drawing parallels to pharmaceutical safety, she emphasizes the importance of understanding the safety and effectiveness of AI systems to harness their value. Addressing AI's applications, she highlights concerns about potential harms, from biased data in facial recognition systems to privacy risks due to data aggregation.

Prabhakar also commends the voluntary commitments by tech companies to meet AI safety standards, noting that government action and legislative responsibility are essential in this effort. While she doesn't provide a specific timeline, she emphasizes the urgency of addressing AI-related issues at the top level of government.