Our books: https://www.nownextlater.ai/aibooks Our Insights: https://www.nownextlater.ai/Insights/ Our AI Academy: https://www.nownextlater.ai/AIAcademy 01:16 Search 02:07 Conversational Aid 03:45 Summarization 03:55 Background Knowledge 05:22 Education 07:19 Coding 09:10 Brainstorming 09:26 Writing Aids 10:25 Fiction Writing 13:13 Defining Your Use Case

Hey there! It's Inês here. I work with businesses on generative AI. Here's something I've observed: - Some leaders are very enthusiastic, seeing huge potential for productivity gains with AI . - Others are cautious, concerned about issues like "the bots hallucinate" and prefer to wait. It's crucial to strike a balance. I recently hosted a webinar where I discuss the real workings of these AI models, what they can and can't do, and how best to approach them. Simple, but informed insights. ⁠ http://www.nownextlater.ai⁠

There are many variables to consider when defining our Generative AI strategy. Having a clear understanding of the use case/business problem is crucial. However, a good understanding of benchmarks and metrics helps business leaders connect with this new world and its potential.

So whether you are intending to: 

• select a pretrained foundation LLM (like OpenAI's GPT-4) to connect via API to your project, 
• select a base open-source LLM (like Meta's Llama 2) to train and customize, 
• or looking to evaluate the performance of your LLM 

the available benchmarks are crucial and useful in this task. In this video we will explore a few examples.

Hey there! It's Inês here. Over the past few weeks I've connected with hundreds of Australian business leaders keen to understand Generative AI's opportunities and risks. Given the interest, I have put together a short briefing of the latest AI news and developments in Australia. I hope you find it valuable.

http://www.nownextlater.ai

Hi there, Today I will share how Generative AI models like ChatGPT are transforming software development. A McKinsey study found that generative AI tools can help developers complete certain programming tasks almost twice as fast. For example, documenting and commenting code took half the time with AI assistance. Writing new code from scratch was 46% faster. And optimizing existing code was 65% faster with the AI tools. These are huge potential gains in productivity. However, the study found the boost was lower for more complex tasks or for junior developers. The AI tools helped senior developers tackle unfamiliar problems 25-30% faster though. The tools also significantly improved the developer experience. Coders reported feeling happier, more fulfilled and “in flow” when using the AI assistants. This is because the tools automated repetitive tasks and provided helpful information quickly. But the study also highlighted some risks and limitations of generative AI for coding. The AI tools sometimes made erroneous recommendations that developers had to double check and fix. The AI didn’t have insight into the organizational context and requirements needed to ensure high quality code. And the AI struggled with more intricate coding tasks that required a big picture view. The opportunity here is for developers actively collaborate with the AI, provide the necessary prompts and context, and review the AI-generated code. Proper training and coaching is key to ensure safe and effective use of these powerful new tools. Overall, AI has incredible potential to boost developer productivity, free up capacity, and improve the engineering experience. But engineering leaders need a thoughtful approach to realize the benefits while managing risks. This includes extensive developer training, expanding use cases beyond just code generation, planning for skill shifts, and implementing governance controls. AI-powered coding is here, but thoughtful human guidance is still essential. See you next time and Stay Human. http://www.nownextlater.ai

In this session we present our Generative AI Transformation Playbook, grounding the approach in research and balancing risk with opportunity.

https://academy.nownextlater.ai By the end of this session the audience should be able to: 1) Understand EU and US regulatory approaches to AI. 2) Recognize the importance of ethics and privacy. 3) Learn about the GDPR and the proposed EU Artificial Intelligence Act. 4) Explore challenges in governing Generative AI, including data erasure and legal bases for data processing. 5) Consider transparency and proportionality in AI governance. 6) Examine EU governance and enforcement mechanisms for AI. 7) Evaluate the EU's regulatory approach as a potential model. 8) Recognize risks for downstream companies using generative APIs, such as data security and legal liabilities. 9) Learn how to implement risk mitigation strategies, including data security measures and ethical guidelines. 10 ) Understand the ongoing nature of AI regulation and the need for collaboration. https://academy.nownextlater.ai

Today’s business world is at a pivotal juncture, spurred by revolutionary technological advancements. Economic uncertainties and job losses exacerbate this complexity. As leaders grapple with balancing strategic cost reductions and smart investments, AI stands out as a promising pathway for decision-makers. The allure of AI as an investment choice is indisputable. Its robustness, tested and proven across predictive models, has shown remarkable returns. This trust in AI’s potential is reflected in Forrester’s predictions, which see AI expenditure growing from $33 billion in 2021 to an astounding $64 billion by 2025. The impressive spending growth doesn’t eliminate the high complexity and risk involved in AI application and adoption. Moving beyond the proven AI capabilities, we encounter the enigmatic domain of Generative AI (Gen AI). This realm, characterized by tremendous hype, speculation, and potential disruptions, demands keen attention. Gen AI includes machine learning models like ChatGPT, Midjourney, Bing AI, Bard, and DALL-E. Trained on massive volumes of text and image data, these models generate new text and images in response to prompts, spurring innovation. Despite the exaggerated narratives, some sectors are recognizing how Gen AI can deliver real value. The technology is poised to bring significant transformations to various sectors, such as IT, marketing and sales, customer service, and product development. www.academy.nownextlater.ai/