Podcast Summary
Generative AI in Photoshop: Addressing the 'empty page problem': Adobe's Firefly Image 3 model in Photoshop lets users generate images directly within the software, addressing the 'empty page problem' and enhancing creativity with powerful text-based editing features.
Adobe's latest Firefly Image 3 model, now integrated into Photoshop, represents a significant leap forward in generative AI for image editing. This isn't just about adding AI capabilities for the sake of it; Adobe identified a common problem for new users - the "empty page problem" - and addressed it by enabling users to generate images directly within Photoshop, allowing them to utilize the software's existing tools for further enhancement. Last year, Adobe introduced the generative fill feature, which let users modify image sections using text prompts. Firefly Image 3 builds upon this foundation with new features, such as the ability to use reference images alongside text prompts, making it a more powerful tool in the image editing landscape. This integration of AI into Photoshop not only enhances the user experience but also opens up new possibilities for creative expression.
Technology companies advance AI with background generation, on-device systems, and smaller models: Adobe simplifies image placement with 'Generate Background' feature, Apple acquires DataCollab for on-device AI, Microsoft introduces smallest AI model PHY 3, SoftBank invests $1B in Japanese language-specific AI
Technology giants like Adobe, Apple, Microsoft, and SoftBank are making significant strides in the field of AI, with a focus on generating backgrounds, on-device AI systems, and smaller, more efficient models. Adobe's new "Generate Background" feature will simplify the process of placing product images in various settings for marketers. Apple has acquired Paris-based startup DataCollab, which specializes in algorithmic compression and embedded AI systems, furthering their goal of running AI models on devices instead of relying on cloud-based systems. Microsoft has launched its smallest AI model yet, PHY 3, which performs as well as larger models and can provide responses with minimal latency. SoftBank is investing nearly $1 billion in developing a world-class Japanese language-specific AI model, reflecting the trend of companies building highly performant LLMs for non-English languages. These advancements demonstrate the continued interest and investment in AI technology across various industries and use cases. Stay tuned for more updates on these developments and the broader AI landscape.
The Exciting Future of AI Agents: Major tech companies are investing in AI agents to increase enterprise spending on AI, as they can execute a strategy from start to finish, including subtasks, making them more effective in convincing businesses to invest in AI.
The development of AI agents is a major focus for both startups and big tech companies in the AI industry. Microsoft, OpenAI, and Google are leading the charge, investing heavily in agentic software as a way to increase enterprise spending on AI. AI agents are different from copilots or assistants because they can execute a strategy from start to finish, including subtasks. For instance, instead of using AI to find the best flight deal and then booking it yourself, you could simply tell an agent to buy the best flight based on certain criteria and have it handle the transaction. The excitement around AI agents began last year with the launch of tools like Auto GPT and Baby AGI, but the conversation ebbed as the technology was still in its early stages. However, the interest has picked up again towards the end of the year, with all the major AI labs recognizing the potential of agentic software in unlocking greater enterprise spending on AI. Microsoft, OpenAI, and Google believe that agents will be more effective in convincing businesses to invest more in AI than what's currently available.
Automating Complex Tasks with AI Agents: Tech companies are developing AI agents to automate complex tasks beyond simple chat interactions, categorized as computer using agents, multistep application agents, and web-based task agents. Companies are taking an incremental approach to launching these agents, focusing on specific workflows to build trust and improve user experience.
Tech companies like Microsoft, OpenAI, Google, and Meta are developing AI agents, or bots, to automate complex tasks beyond simple chat interactions. These agents can be categorized into three types: computer using agents, which can take over a user's computer and operate different applications; multistep application agents, which can carry out multiple-step tasks within an application without human oversight; and web-based task agents, which can complete web-based tasks requiring communication with different applications. Companies are taking an incremental approach to launching these agents, focusing on specific workflows to avoid overpromising and build trust. For example, Microsoft is building an agent within its Dynamics app for salespeople that suggests multistep actions the app can take. This approach allows for more effective automation and better user experience.
Advancements in AI: Large Language Models, Grounding, and Multi-Agent Collaboration: Large Language Models can generate synthetic data, grounding enables AI models to validate outputs, and multi-agent collaboration breaks down complex tasks into subtasks for optimal performance.
The field of AI is witnessing significant advancements that are expanding the capabilities of AI agents. Ion Stoica, a co-founder of AnyScale and Databricks, discussed two such advancements. The first is the improvement in developers' ability to use Large Language Models (LLMs) to generate synthetic data for problem solving and reasoning within specific parameters. The second is the emergence of grounding, a process that enables AI models to automatically verify the validity of other models' outputs. This validation allows LLMs to improve their own outputs, leading to a significant jump in problem-solving abilities. Andrew Ng, the co-founder of Coursera and a former head of AI at Baidu and Google Brain, also touched upon this topic. He emphasized the importance of multi-agent collaboration as a key AI agentic design pattern. In this approach, complex tasks are broken down into subtasks, and different agents, possibly LLMs, are assigned to accomplish each subtask. This method, which has proven effective for many teams, allows for optimal subtask performance and provides a framework for developers to tackle complex tasks. In essence, these advancements in LLMs, grounding, and multi-agent collaboration are revolutionizing the way AI agents function and solve problems. They are enabling developers to create more efficient, effective, and intelligent agents, ultimately leading to significant improvements in AI's ability to understand and solve complex tasks.
AI agents gaining momentum in tech industry: Excitement around AI agents as a new extension of workflow automation and potential future of symbiotic relationship between humans and AI
AI agents are gaining momentum in the tech industry, as evidenced by the recent practices and explorations in this area. Robert Scoble's retweet of Taskade, an AI agent tool for coordinating tasks, is just one example. Another project, Payman, aims to give AI agents the ability to pay humans for tasks they cannot do, envisioning a symbiotic relationship between humans and AI agents. However, Pedro Domingos raises a caution that agents have been a decades-old idea in AI with limited progress due to complexity. Despite this, the current technological capacity, energy, and specificity of experiments suggest that things might be different this time. The excitement around AI agents is not just driven by enterprise spending, but also by their potential as an extension of workflow automation and reimagining. While not every process will be agentized, the potential benefits are significant, and the future looks promising for this area of AI development.
![Jeremiah Lowin: Explaining the New AI Paradigm - [Invest Like the Best, EP.307]](https://www.podcastworld.io/podcast-images/invest-like-the-best-with-patrick-o-shaughnessy-3pmywn1q.webp)