Matteo Castiello’s Post

Nice short summary of how AI is creating interfaces.

Is ElevenLabs now the "conversational ai industry killer?" Having spent nearly a decade in Conversational AI, I have seen countless "off shelf" "no code" solutions. None of which have delivered what the commercial said. What is different this time is that LLMs have eliminated the need to much of the heavy design aspects of conversational ai. Upfront strategy, creative direction, API / server integration, UX, and go to market are whats most important now. ElevenLabs agent creator allows you to bring some of these components together, and for many this will be a widget creator. My biggest questions are around the "knowledge upload" and "integrating your own server." These have been the hardest aspects of our recent manual LLM builds, and I suspect organizations will not be able to simply plug and play their knowledge base, APIs, or servers without some sophisticated development. The front end experience will still need to be great, and integrated into the organizations channels as well. What this tells me is that we are continuing to move in a direction where much of the minutia is being absorbed by these platforms, allowing brands to focus on the hard parts: Strategy, Integration, UX, go to market, support of their AI support, knowledge organization, etc. We'll be digging in to this tool for sure. Exciting times ahead.

Have you been hearing about AI agents everywhere but still wondering, "What the heck are AI agents?" Imagine having a digital assistant that not only automates tasks but also learns, adapts, and makes decisions just like a human. AI agents are transforming the way businesses operate, making processes faster, smarter, and more efficient. By integrating AI agents into your operations, you can streamline complex workflows, enhance customer interactions, and gain a competitive edge. Picture an AI agent handling your customer support 24/7, making data-driven decisions, and even predicting market trends to keep you ahead of the curve. Ready to dive into the world of AI agents? Here are some key points to get you started: Definition: AI agents are software entities that perform tasks autonomously, learn from data, and adapt to new information. Applications: From customer service chatbots to advanced predictive analytics, AI agents are used across various industries. Benefits: Improved efficiency, reduced costs, and enhanced decision-making capabilities. Tools to Get Started: Code Tools:  -TensorFlow: An open-source platform for machine learning.  -PyTorch: A deep learning framework for building AI agents.  -OpenAI GPT-4: A powerful language model for creating conversational AI agents. No-Code Tools:  -Bubble: Build web applications with complex functionalities without writing code.  -Zapier: Automate workflows by connecting different apps and services.  -Lobe: Train machine learning models with an easy-to-use visual interface without any coding required. Main Players Implementing AI Agents: Dharmesh Shah's Agent.AI: A pioneering platform that leverages AI agents to provide intelligent customer support. Google: Utilizing AI agents for personalized user experiences and advanced analytics. Amazon: Employing AI agents through Alexa for smart home and business applications. IBM: Using Watson AI agents for enterprise solutions and data analytics. AI agents are the future of efficient and intelligent business operations. Let's connect and discuss!

Build generative AI experiences with Vertex AI Agent Builder

At Lab49 we are spending a lot of time thinking about what’s next for generative AI.    In a recent presentation, Microsoft strategist Chris Coulthrust made this point: the key to leveraging AI will be design. What Coulthrust meant was that while everyone right now is focused on the capabilities of generative AI models, soon our attention will shift to the design of applications as the most critical aspect of their performance. Conversational AI interfaces are the first new UI paradigm in over 60 years. That’s according to Jakob Nielsen and his partner Don Norman, who wrote the book “The Design of Everyday Things.” Every UX designer knows “The Nielsen 10” usability principles. Putting AI in that context underscores the importance and high stakes for business leaders of getting it right. In the financial sector, people are just starting to come to terms with how this will change not just the information that’s available to advisors, traders, and portfolio managers, but their workflow. We saw something similar with the emergence of mobile when information became available at our fingertips and was aware of our surroundings. Mobile allowed us to leverage push notifications which started changing behavior. Like mobile, AI is changing the way we do things and will fundamentally change our lifestyles.  It may require major revamps in the curation of data and the design of platforms to take advantage of the efficiencies that this new “democratization” of knowledge offers. Our belief at Lab49 is that companies should also be thinking about answering questions clients have before they have been asked. Right now, people “prompt” chat boxes by typing in queries and getting answers that summarize large volumes of data. In the future, the AI will prompt people with ready-made drafts, next steps, and new ‘magic wand’ interactions. We are seeing these features come up first in customer servicing applications, but it goes way beyond traditional call centers or robo-chats. Imagine you are a trader at a large financial institution. One of the key parts of your job is to provide customers with “market color,” or commentary on swings in the bond or stock market. AI models can monitor market fluctuations and combine those with transaction history, chat transcripts, and comments from sales notes stored in CRMs. LLMs can detect sentiment, tone, and other themes from the conversations. Now combine all of that with data from “indications of interest”. Those IOIs are the preliminary orders customers set up to buy or sell securities. That information would put any trader or salesperson in a position to generate market color, at scale, that is much more targeted and useful to their individual customer. You could imagine layering in data from news applications and even Twitter or Reddit. All of this requires a tremendous amount of coordination and design to be seamless. It also offers a huge competitive advantage. 

Unleash the Power of Complex AI Workflows with Azure AutoGen & AI Bots Azure AutoGen and AI bots are revolutionizing AI development! This blog dives into how this powerful duo simplifies building sophisticated AI applications. Learn about AutoGen's multi-agent capabilities and Promptflow's streamlined development process. Discover: - How to overcome limitations of traditional, single-agent workflows - A step-by-step guide to building and deploying AutoGen workflows - Best practices for successful implementation Embrace the future of AI - Read now! https://buff.ly/3XACJJl #AzureAutoGen #MachineLearning #AIDevelopment #AlwaysApexon Author: Darshan Kirani.

We have a new #eureka moment which the below article gives a good insight on. The Eureka platform from SymphonyAI has evolved to run both generative and predictive AI applications across Retail, Business IT, Industrial, and Financial Services. Enterprises today are keen to leverage the benefits of generative and predictive AI, and Eureka provides just that. With Eureka, businesses can incorporate proprietary information into the datasets used to train the LLMs and customise these models to fit their specific industries, in an affordable way. Looking forward to seeing how Eureka is changing the game for AI applications in various industries and most importantly, what else we, at SymphonyAI Financial Services can do to bring AI-led innovation to our customers. https://lnkd.in/eWrTy8WR

Copilot is the new UI for AI As serious players in the AI space begin rebuilding entire enterprise solutions with AI enablement from the ground up, we will gradually see the significant benefits of this next phase in AI evolution. Bolting on Generative AI features is just the beginning, with so much more to come. Microsoft's own Co-Pilot tool and Chat GPT 4.0 have just released their latest quarterly data, outlining the most recently recorded improvements, which included response rates being generated 2 faster, response satisfaction rates 3 X higher, and this is all coming from more than 700 product updates and 150 new features in just the last few months. Co-Pilot and Open AI is learning and evolving at such a phenomenal rate. Most of us have already experienced the impact that Co-Pilot type solutions are delivering on a day-to-day basis, like turning organisational content into a rich database of information and insights, or enabling you to collaborate with Copilot like a partner and turning everyday business interactions into reusable business assets. Microsft is now talking about the imminent launch of its 'Advanced Reasoning' feature within its open AI tool, and expanding Co-Pilot's range to include Excel, Vizualisation charts, and tables. Microsoft openly confesses to "taking everything we’re learning from our customers, and using it to make Copilot even better". And this gives us an insight into the future power of Self Learning and Self Optimising AI models that are being embedded into and across the enterprise, that will further transform AI's business capabilities. It's exciting and scary at the same time.

You may have heard that Generative AI narrows the skill gap between top and bottom performers - usually by increasing the performance of the bottom 25% without moving the needle for the top 25%. This makes intuitive sense. An expert software engineer with 25 YoE won't see 10x gains in their problem-solving ability with some fancy auto-complete or help with boiler-plate code. However, a junior engineer (or hobbyist, like myself) might see huge gains from an always-available AI pair programmer that can take us from 10% to 50% productive. But I want to reframe this concept. Yes, Generative AI nominally raises the skill floor in certain domains, but more importantly, it opens the door to new skills for people who could not or would not otherwise access them. It reduces exclusion and increases accessibility. Take me, for example. When we started Wallabi, I had minimal marketing skills. With Generative AI, I went from useless to (somewhat) capable pretty quickly. I didn't know what bounce rate was, how to conceptualize & track a marketing funnel, how to build a website, how to work with Google Analytics, or how to segment audiences & target content. Without Generative AI, I would have had no clue where to start, how to break in, or what to do. But it took me from 10% to 50% in the span of a few weeks for what otherwise would have taken me months or years. Am I the world's best marketer? Absolutely not, and I never will be. But am I Good Enough (TM)? I think so :) Generative AI will not replace experts anytime soon, but it will make some domains accessible to more people than ever before. If you've ever wanted to dip your toe into visual arts, music production, video production, academic research, data & analytics, and software development, there are new tools built for you hitting the market just about every day. It's a great time to scratch that curiosity itch!

Up to 50% productivity gains on certain tasks using AI ! As you know , I have been active in the field of AI lately. Let me share some thoughts: in the rapidly evolving landscape of technology, generative AI does stand as a beacon of transformative potential, reshaping how businesses operate and unlocking new avenues for productivity enhancement. As OpenAI's GPT-4 marks its first anniversary, we've witnessed a significant uptick in the market capitalization of the technology sector, spurred by AI-driven innovations. Companies like Mistral AI in France, NVIDIA, Alphabet Inc., Amazon, and Microsoft are at the forefront, developing and deploying AI technologies that promise to revolutionize industries across the board. Generative AI, with its capacity to create, simulate, and innovate, is gradually making its mark across various sectors. From banking and consulting to film production, the cautious yet deliberate adoption of these tools hints at a future where the mundane is automated, and the complex is simplified. While we stand on the cusp of this AI-induced productivity surge, notable examples illuminate the path forward. As the graph enclosed from The Economist shows, widespread adoption is still a long way ahead ! JPMorgan Chase & Co. Chase, with over 300 AI use cases, and Bayer, boasting 700, exemplify how deeply AI can penetrate industry operations. Capgemini's collaboration with Google Cloud to develop an extensive library of industry use cases further underscores the momentum building around generative AI's practical applications. Yet, it's not just about the giants. Amdocs and Sprinklr demonstrate how AI can dramatically improve customer service efficiency, proving that even straightforward implementations can yield significant benefits. Nasdaq's use of AI to streamline financial crime investigation and Microsoft's GitHub Copilot, accelerating software development, are testament to AI's vast applicability. Pinterest and L'Oréal are harnessing generative AI to refine user engagement and marketing strategies, showcasing the technology's versatility. These examples are just the tip of the iceberg, signaling a shift towards more intelligent, efficient, and productive business operations. As we embrace generative AI, it's crucial to navigate the challenges it presents, from data privacy concerns to the need for a skilled workforce adept at harnessing these tools. The journey towards widespread AI adoption may be gradual, but the potential for transformative impact on productivity and innovation is undeniable. Smaller companies must quickly jump on the bandwagon! Let's engage in a dialogue about integrating generative AI into our workflows and strategies. How are you planning to leverage AI in your business operations? The future is here, and it's powered by artificial intelligence. #ArtificialIntelligence #GenerativeAI #Productivity #Innovation https://lnkd.in/gCfyEcR5