Tech Uncensored: Large Language Models – Myths, Facts and the Future of AI

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Large Language Models (LLMs) have emerged as one of the most powerful and versatile artificial intelligence technologies of our time. By training massive neural networks on vast datasets of human-generated text, LLMs have developed an unprecedented ability to understand and generate human-like language with robust fluency and comprehension. This breakthrough has unlocked a wide range of innovative applications across industries, from content creation and language translation to conversational AI assistants and code generation.

More recently Open AI released ChatGPT 4o that they say can reason across different modalities in real time. They trained a single new model end-to-end across text, vision, and audio, meaning that all inputs and outputs are processed by the same neural network. This is still early days but this idea of developing a multi-modal model has vast potential to create much more effective outputs that can help yield better decision making.

The nascency of this technology has yet to be fully understood–language, image, audio understanding, the generation capabilities that can drive substantial productivity gains, and enable new forms of human-machine collaboration and even question which human jobs are replaceable– are still emerging.

As well, LLM technology has limitations and risks including issues of factual inaccuracies, biases inherited from training data, lack of common-sense reasoning, and pervasive potential for misuse, and more recently the data privacy implications that we’ve seen from OpenAI’s unconsented use of Scarlett Johansson’s voice.

Techniques like Retrieval Augmented Generation (RAG) are highlighted as promising approaches to enhance LLMs' knowledge grounding, improve their accuracies over time.

We welcomed ?? Amir Feizpour , CEO and founder of AI.science , a platform for expert-in-the-loop business workflow automation. In this episode of Tech Uncensored, we will delve into the transformative impacts of LLMs across sectors, the applications both present and future, the current challenges and risks and what does this mean to startups developing in this space.

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The LATEST in Tech News:?

• Apple is gearing up for its Worldwide Developers Conference (WWDC) from June 10-14, 2024. The event is expected to showcase significant updates to iOS 18, including major AI enhancements, Siri upgrades, and new features for Apple Music and Maps.
• Analysts at Bernstein have labeled Big Tech companies like Google, Amazon, Microsoft, Meta, and Apple as the new "Big Oil" due to their planned capital expenditures surpassing $200 billion in 2024. This investment, focused heavily on artificial intelligence with estimates that over the next five years, Big Tech is on track to spend $1 trillion in CapEx.
• The European Union has passed the first legislation aimed at regulating artificial intelligence. This law seeks to address ethical concerns and ensure responsible AI usage, marking a critical step in global tech regulation. "This flagship legislation – the world’s first standalone law governing the use of AI – assumes a risk-based approach i.e., the higher the risk of harm to society an AI use case poses, the more onerous the rules that are triggered."
• Adobe has faced backlash from artists over new terms in Photoshop that impact how they use the software. "One sentence in the pop-up has raised hackles among creative professionals: 'Clarified that we may access your content through both automated and manual methods, such as for content review.'"This has caused significant concern in the creative community regarding usage rights and subscription terms.
• Collision is coming to Toronto June 17-20, 2024. This will be the fourth and likely final year Toronto will host the North American tech conference. Collision 2023 hosted more than 36,000 attendees and nearly 1,500 startups.

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