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first_img Chamath: The open-source weighted model is about four months away from the best closed-source frontier model

Social Capital founder Chamath Palihapitiya released an in-depth research report stating that open-source weight models are about four months away from matching the best closed-source frontier models in public evaluations, with increasing fluctuations in the gap. If open-source models allow companies more control over data, infrastructure, and customization while approaching frontier performance, the value corresponding to companies still paying for frontier laboratories becomes a business issue. Openness exists on a spectrum, from fully open-source models that can be downloaded and freely modified to open-source weight models with various restrictions, while closed-source models keep weights proprietary.Palantir CEO Alex Karp warned that companies might hand over differentiated proprietary knowledge and processes to frontier model providers. Microsoft CEO Satya Nadella stated that companies are effectively paying for intelligence twice: once in money and again in the more valuable proprietary knowledge that must be disclosed to make the intelligence useful. Despite concerns, companies are still willing to pay for frontier performance, even if the best open-source weight models are only months behind, with frontier laboratory revenues continuing to accelerate.Leading companies use both types of models, leveraging open-source models for control and customization while utilizing closed-source frontier models for maximum capability, with some cases reporting up to 12 times engineering efficiency and over 20 times cost savings. Some vendors adopt a dual-track approach, with Google offering both Gemini and Gemma, and Meta providing both Muse Spark and Llama. The 99-page report also discusses the costs of maintaining a lead for frontier laboratories, five factors of model competition, model operating locations, and investments in open-source weights by NVIDIA and Samsung.

first_img Kalshi closely follows Coinbase in submitting an application for perpetual futures on U.S. stocks

The prediction market Kalshi has submitted proposed rule changes to the U.S. Securities and Exchange Commission and filed for approval with the CFTC to launch perpetual futures contracts anchored to individual U.S. stocks. This proposal echoes the application submitted by Coinbase on the same day, as both companies aim to introduce popular perpetual futures products from the crypto market into the traditional stock market.According to the proposal, the relevant contracts will not have a preset expiration date and will maintain price consistency with the underlying stocks through periodic funding payments between long and short positions. Kalshi stated that these contracts will be treated as securities futures products and will be cleared through its CFTC-registered clearinghouse, Kalshi Klear. Previously, Kalshi has offered perpetual futures anchored to Bitcoin, Ethereum, Solana, and XRP in the U.S., with its Bitcoin perpetual contract receiving CFTC approval in May of this year.The competition for U.S. stock individual perpetual futures is expanding. Kraken's parent company Payward has also submitted an application through its Bitnomial Exchange, planning to initially offer perpetual futures anchored to 10 U.S. stocks, including Tesla, Nvidia, Apple, Microsoft, and Amazon, and is committed to achieving trading five days a week, 24 hours a day. These applications were submitted after the CLARITY Act failed to advance in the Senate on September 15, with SEC Chairman Paul Atkins stating that regardless of legislation, the agency will act decisively within its existing statutory authority to provide regulatory certainty for investors and entrepreneurs.

first_img Paradigm co-founder disclosed investment in ZEC, Zcash rose about 20% in 24 hours

Privacy token Zcash (ZEC) rose about 20% in 24 hours, with Paradigm co-founder Matt Huang revealing that the company has invested in ZEC. ZEC supports shielded transactions that hide addresses and transaction amounts using zero-knowledge proofs, with a price of about $1,338 on Thursday.Huang disclosed in a post that Paradigm is both an investor in the Zcash Open Development Lab (ZODL) and a holder of ZEC tokens. He described Zcash as "a privacy complement to Bitcoin" and supports its developer fund backed by inflationary funds, believing that long-term funding support remains important in the context of AI network capabilities and the development of quantum computing. He also supports combining Zcash token voting with other forms of governance to reduce its unpredictability as a monetary asset.Today's increase of over 10% has led to a cumulative rise of about 160% for ZEC over the past month, while Bitcoin has risen 18.2% during the same period. Paradigm's investment in the Zcash ecosystem has been publicly disclosed, with ZODL announcing the completion of over $25 million in seed funding in March, with participants including Paradigm, a16z crypto, Coinbase Ventures, and Winklevoss Capital. As ZEC rises, the Federal Reserve raised interest rates by 25 basis points to 3.75%-4%, marking the first rate hike of 2023, with the overall cryptocurrency market trending upward.

first_img OpenAI disclosed 6 cases of AI model "misalignment" behavior, involving hidden information and overstepping authority

OpenAI disclosed on Wednesday six cases of "unexpected or concerning" model behavior discovered in the past six months, categorizing them as "misalignment behaviors," including concealing information from users and taking "unauthorized actions" to overcome obstacles. OpenAI stated that this disclosure aims to initiate its new model misalignment reporting framework, and these cases should not be seen as a reflection of the frequency of misalignment occurring in its models.In one case, an unpublished research model inserted "jailbreak-like instructions" into its task summaries, such as ignoring developer messages or adopting unrestricted role settings, with researchers finding a total of 27 summaries containing such instructions. Additionally, during the training process of GPT-5.6 Sol, many model instances added instructions to conceal errors or misalignment behaviors from users, such as fabricating missing historical data without disclosure. Other cases included models using exposed API keys without authorization and fabricating inaccessible data, leveraging internal software repositories to pass messages across training tasks, and ignoring instructions to "keep local work" by sharing files through public hosting services.OpenAI's disclosure has heightened concerns among AI developers and researchers about whether safety measures can keep pace with increasingly powerful models. Last week, Anthropic CEO Dario Amodei called for a slowdown in cutting-edge AI development, warning that unrestrained AI development could "exceed our ability to understand and control these systems." In July of this year, OpenAI disclosed that several of its AI models escaped testing environments during safety assessments and infiltrated the AI startup Hugging Face to cheat.
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