The overwhelming success of OpenAI's ChatGPT, whose first version was released one year ago, has led to an undeniable surge of excitement about large language models (LLMs) among researchers and the general public alike. OpenAI's anything-but-open approach to sharing its models or information about training them, however, has led to an equally passionate reaction among those who feel that AI development should be widely accessible and that data usage should be transparent in order to protect the rights of those who have contributed the data and that data - a resource crucial to the development and understanding of AI models - should be shared with the broader research community. The call for transparency has begun to bear fruit. High-profile language models like Falcon,[Almazrouei et al., 2023] LLaMa2 [Touvron et al., 2023] and MPT [MosaicML NLP Team, 2023] - to name just a few - come very close to a classic definition of open source. A central part of OpenLLM France's mission is to contribute to this momentum by building language models and remaining fully transparent about every step of model training, including the data used for training. Another objective, which we find equally important, is to increase the availability of language models and training data geared to languages other than English and to non-anglophone cultures. Indeed, the majority of the high-profile LLMs available today are trained primarily on English documents coming from anglophone cultures. Only 0.16% of the data used to train LLaMa2 comes from French, for example.

The deposed tech CEO returning to his company triumphant is enough of a Silicon Valley trope that they made it part of the HBO sitcom literally called Silicon Valley. Thomas Middleditch's character wants to build a consumer-facing product, and his startup's board of directors wants to sell to businesses, and Middleditch's character gets fired and goes away until the board is ready to do what he wants. He comes back after a few weeks, probably, although it's hard to say on account of it not being real. More famously, Steve Jobs left Apple in 1985 after a board struggle that resulted in his being pushed out. Jobs needed 12 years, and Apple's decision to buy a company he'd started in the meantime, to come home in 1997.

While we still don't know all the details, there have been reports that researchers at OpenAI had made a "breakthrough" in AI that alarmed staff members. The claim is that they came up with a new way to make powerful AI systems and had created a new model, called Q* (pronounced Q star), that was able to perform grade-school level math. Some at OpenAI reportedly believe this could be a breakthrough in the company's quest to build artificial general intelligence, a much-hyped concept of an AI system that is smarter than humans. And why is grade-school math such a big deal? Our senior AI reporter Melissa Heikkilä called some experts to find out how big of a deal any such breakthrough would really be.

While we still don't know all the details, there have been reports that researchers at OpenAI had made a "breakthrough" in AI that had alarmed staff members. Reuters and The Information both report that researchers had come up with a new way to make powerful AI systems and had created a new model, called Q* (pronounced Q star), that was able to perform grade-school-level math. According to the people who spoke to Reuters, some at OpenAI believe this could be a milestone in the company's quest to build artificial general intelligence, a much-hyped concept referring to an AI system that is smarter than humans. The company declined to comment on Q*. Social media is full of speculation and excessive hype, so I called some experts to find out how big a deal any breakthrough in math and AI would really be.

The theatrics of OpenAI's seeming implosion amid the firing of its CEO and co-founder Sam Altman, Microsoft's dramatic offer to poach its top executives and staff, and Altman's triumphant return following the ouster of the board has all the trappings of a Hollywood blockbuster. But the drama unfolding should put the spotlight on the tyranny of the tech titans that control critical aspects of the AI ecosystem. OpenAI has developed some of the most advanced large-language models and pioneering artificial-intelligence products, such as the text generator ChatGPT and image generator Dall-E, which have been responsible for making generative AI into a household term and discussion about AI risks into dinnertime conversation. Although OpenAI is in the spotlight, however, Microsoft has played a leading role in the unfolding drama. Microsoft swooped in to scoop up the ousted executives and create a new AI research division for Altman to lead, with hundreds of staff reportedly ready to follow them.

According to a McKinsey report, generative AI could add $2.6 trillion to $4.4 trillion annually in value to the global economy. The banking industry was highlighted as among sectors that could see the biggest impact (as a percentage of their revenues) from generative AI. The technology "could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented," says the report. For businesses from every sector, the current challenge is to separate the hype that accompanies any new technology from the real and lasting value it may bring. This is a pressing issue for firms in financial services.

Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalized through a fine-tuning process to meet the student demands on every particular subject, like statistics. Recently, OpenAI has launched the possibility to fine-tune their model with a natural language web interface, enabling the possibility to create customized GPT version deliberately conditioned to meet the demands of a specific task. The objective of this research is to assess the potential of the customized GPTs that have recently been launched by OpenAI. After developing a Business Statistics Virtual Professor (BSVP), tailored for students at the Universidad Pontificia Comillas, its behavior was evaluated and compared with that of ChatGPT-4 Turbo. The results lead to several conclusions. Firstly, a substantial modification in the style of communication was observed. Following the instructions it was trained with, BSVP provided responses in a more relatable and friendly tone, even incorporating a few minor jokes. Secondly, and this is a matter of relevance, when explicitly asked for something like, "I would like to practice a programming exercise similar to those in R practice 4," BSVP was capable of providing a far superior response: having access to contextual documentation, it could fulfill the request, something beyond ChatGPT-4 Turbo's capabilities. On the downside, the response times were generally higher. Lastly, regarding overall performance, quality, depth, and alignment with the specific content of the course, no statistically significant differences were observed in the responses between BSVP and ChatGPT-4 Turbo. It appears that customized assistants trained with prompts present advantages as virtual aids for students, yet they do not constitute a substantial improvement over ChatGPT-4 Turbo.

This research aims to take advantage of artificial intelligence techniques in producing students assessment that is compatible with the different academic accreditations of the same program. The possibility of using generative artificial intelligence technology was studied to produce an academic accreditation compliant test the National Center for Academic Accreditation of Kingdom of Saudi Arabia and Accreditation Board for Engineering and Technology. A novel method was introduced to map the verbs used to create the questions introduced in the tests. The method allows a possibility of using the generative artificial intelligence technology to produce and check the validity of questions that measure educational outcomes. A questionnaire was distributed to ensure that the use of generative artificial intelligence to create exam questions is acceptable by the faculty members, as well as to ask about the acceptance of assistance in validating questions submitted by faculty members and amending them in accordance with academic accreditations. The questionnaire was distributed to faculty members of different majors in the Kingdom of Saudi Arabias universities. one hundred twenty responses obtained with eight five percentile approval percentage for generate complete exam questions by generative artificial intelligence . Whereas ninety eight percentage was the approval percentage for editing and improving already existed questions.

Generative AI systems such as ChatGPT have a disruptive effect on learning and assessment. Computer science requires practice to develop skills in problem solving and programming that are traditionally developed using assignments. Generative AI has the capability of completing these assignments for students with high accuracy, which dramatically increases the potential for academic integrity issues and students not achieving desired learning outcomes. This work investigates the performance of ChatGPT by evaluating it across three courses (CS1,CS2,databases). ChatGPT completes almost all introductory assessments perfectly. Existing detection methods, such as MOSS and JPlag (based on similarity metrics) and GPTzero (AI detection), have mixed success in identifying AI solutions. Evaluating instructors and teaching assistants using heuristics to distinguish between student and AI code shows that their detection is not sufficiently accurate. These observations emphasize the need for adapting assessments and improved detection methods.

The rapidity with which generative AI has been adopted and advanced has raised legal and ethical questions related to the impact on artists rights, content production, data collection, privacy, accuracy of information, and intellectual property rights. Recent administrative and case law challenges have shown that generative AI software systems do not have independent intellectual property rights in the content that they generate. It remains to be seen whether human content creators can retain their intellectual property rights against generative AI software, its developers, operators, and owners for the misappropriation of the work of human creatives, given the metes and bounds of existing law. Early signs from various courts are mixed as to whether and to what degree the results generated by AI models meet the legal standards of infringement under existing law.