Is OpenSource AI Threatening The Tech Titans?

Forbes Innovation AI Is OpenSource AI Threatening The Tech Titans? Cindy Gordon Contributor Opinions expressed by Forbes Contributors are their own. CEO, Innovation Leader Passionate about Modernizing via AI Following Jun 18, 2023, 05:54pm EDT | Press play to listen to this article! Got it! Share to Facebook Share to Twitter Share to Linkedin OpenSource Navigation Image getty Open-source AI can be defined as software engineers collaborating on various artificial intelligence projects that are open to the public to develop. The goal is to better integrate computing with humanity.

In early March, the open source community got their hands on Meta’s LLaMA which was leaked to the public. In barely a month, there are very innovative OpenSource AI model variants with instruction tuning , quantization , quality improvements , human evals , multimodality , RLHF , etc. Open-source models are faster, more customizable, more private, and capable.

They are doing things with $100 and 13B params that even market leaders are struggling with. One open-source solution, Vicuna, is an Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality. Who will want to pay major premiums for OpenAI Chat GPT4 is there are open-source options performing at 90% comparisons when it is free? Open-source AI is gaining on Google and ChatGPT, according to a new report from The Information.

The report found that open-source AI models are now “reasonably close” in performance to proprietary models from Google and ChatGPT creator OpenAI. The report’s findings are significant because they suggest that open-source AI is becoming a viable alternative to proprietary AI models. This could have a major impact on the AI industry, as it could lead to more competition and innovation.

MORE FOR YOU Staying Ahead Of ChatGPT And Changes To Workplace Productivity It’s No Joke That Generative AI Being Able To Generate Rip-Roaring Humor Is A Serious Sign Of Approaching Human Sensibility, Says AI Ethics And AI Law AI Could Automate 70% Of Work, ‘Head Of Remote’ Roles And Adobe’s HR Chief On Its No-Mass-Layoffs Playbook The report’s author, Ion Stoica, a professor of computer science at University of California, Berkeley, said that the rise of open-source AI is due to a number of factors, including the increasing availability of data and computing resources. Stoica also said that open-source AI is more accessible to developers, which is leading to a wider range of applications being developed. The report’s findings are good news for businesses and developers who are looking for affordable, ethical and accessible AI solutions.

Open-source AI models offer a viable alternative to proprietary models, and they are likely to become even more popular in the future. Here are some additional details from the article: Open-source AI models are developed by a community of developers, rather than a single company. This means that they are more transparent and accountable.

Open-source AI models are often more affordable than proprietary models. This is because they are not subject to the same licensing fees. Open-source AI models are more flexible and customizable than proprietary models.

This is because they are not locked into a specific platform or framework. The rise of open-source AI is a positive development for the AI industry. It is making AI more accessible, affordable, and transparent.

This is leading to a wider range of applications being developed, which is benefiting businesses and consumers alike. This all being said, a great article by MIT writer, Will Douglas Heaven, cautioned that although there is tremendous collaboration amongst the open source community, with the legal risks and concerns on AI, perhaps changes may be needed. As stated in his research article where he quotes Dave Willner, head of OpenAI’s trust and safety teams.

“It’s more that we’re trying to figure out how to reconcile transparency with safety. And as these technologies get more powerful, there is some amount of tension between those things in practice. ” .

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“A lot of norms and thinking in AI have been formed by academic research communities, which value collaboration and transparency so that people can build on each other’s work ,“ says Willner. Whilst we work to reconcile a stronger balance, OpenSource continues to bring tremendous value to businesses to leverage AI methods that are more cost-effective and enabling access is key in this fast paced and more intelligent world we are now living in. OpenSource AI Value to Business Here are some examples of how open-source AI is being used to benefit businesses and consumers: Healthcare: AI is being used to develop new diagnostic tools and treatments for diseases.

For example, AI is being used to develop new cancer detection tools that can identify tumors earlier than traditional methods. AI is also being used to develop new treatments for diseases, such as personalized cancer therapies. Finance: AI is being used to improve fraud detection and risk assessment .

For example, AI is being used to identify fraudulent transactions in real time. AI is also being used to assess the risk of lending money to businesses and consumers. Customer service: AI is being used to automate customer service tasks, such as answering questions and resolving issues.

For example, AI is being used to create chatbots that can answer customer questions 24/7. AI is also being used to develop new tools that can help customer service representatives resolve issues more quickly. Product development: AI is being used to develop new products and services that are more personalized and efficient.

For example, AI is being used to develop new recommendation engines that can suggest products that customers are likely to be interested in. AI is also being used to develop new manufacturing processes that can produce products more efficiently. Conclusion These are just a few examples of how open-source AI is being used to benefit businesses and consumers.

AI is a powerful technology that has the potential to revolutionize many industries. As AI continues to develop, we can expect to see even more innovative applications that improve our lives. How Google, OpenAI, a now a for profit organization and Meta will embrace these escalating market dynamics remains to be seen.

Research Notation: Special Thank you to Rehmat Orakzai for his research contributions to advocating the value of OpenSource AI and his strong contributions to this research article. References: Afzal, A. and Patel, D.

A. ” We Have No Moat, And Neither Does OpenAI ” Leaked Internal Google Document Claims Open Source AI Will Outcompete Google and OpenAI, May 4, 2023. del Principe, A.

Ten Top Open AI Platforms to Try. Heaven, Will D. The open-source AI boom is built on Big Tech’s handouts.

How long will it last? MIT Review. Examples of Other OpenAI Model Sources Many of the OpenAI models are built on top of LLaMA, an open-source large language model released by Meta AI. Others use a massive public data set called the Pile, which was put together by the open-source nonprofit EleutherAI.

Hugging Face , a startup that champions free and open access to AI, its chatbot, HuggingChat, is built on top of an open-source large language model fine-tuned for conversation, called Open Assistant, that was trained with the help of around 13,000 volunteers and released in March 2023. It is also built on Meta’s LLaMA. StableLM , is an open-source large language model released also in March 2023 by Stability AI, the company behind the hit text-to-image model Stable Diffusion .

Stability AI released StableVicuna, a version of StableLM that—like Open Assistant or HuggingChat—is optimized for conversation. (note: StableLM as Stability’s answer to GPT-4 and StableVicuna its answer to ChatGPT. ) And yes there are more: Alpaca (from a team at the University of Stanford) Cerebras-GPT (from AI firm Cerebras).

Most of these models are built on LLaMA or datasets and models from EleutherAI; Cerebras-GPT follows a template set by DeepMind. Dolly (from the software firm Databricks) LoRA is a powerful technique as it represents model updates as low-rank factorizations, which reduces the size of the update matrices by a factor of up to several thousand. This allows model fine-tuning at a fraction of the cost and time.

Being able to personalize a language model in a few hours on consumer hardware is breakthrough innovation, particularly for aspirations that involve incorporating new and diverse knowledge in near real-time . Definition of Terms: Artificial Intelligence is a branch of computer science that develops programs and algorithms (step-by-step processes designed to solve a problem or answer a question) that help make various machines operate in more human-like ways. There are several subfields of this science, including: Natural language processing (NLP) , which develops natural interactions between humans and computers.

NLP is software which helps machines process human language, create understandable words, and interacts with humans through language. Machine learning (ML) prioritizes a machine’s ability to analyze information and makes recommendations or decisions based on the data sets provided. Computer vision is about creating machines that can understand and interpret visual information (images, pictures, etc.

) Robotics , physically perform tasks without human micro-management, including interaction with humans. Robots are ideal for highly routine and repetitive tasks. Follow me on Twitter or LinkedIn .

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