Archive 22.04.2024

The study highlights the rapid progress and transformative potential of AI in weather prediction.

It takes a village to nurture social robots. Researchers who develop social robots—ones that people interact with—focus too much on design features and not enough on sociological factors, like human-to-human interactions, the contexts where they happen, and cultural norms involving robots, according to an award-winning paper from Cornell and Indiana University scholars who specialize in human-robot interaction.

FAULHABER is introducing a new Motion Control System. More precisely: The world's smallest Integrated Motion Controller.

Unleashes AI Upgrade

Facebook’s parent company Meta has released newly upgraded AI designed to eclipse ChatGPT.

Dubbed Llama 3, the AI engine will ultimately be used to power Meta AI — a chatbot designed by Zuckerberg’s company to compete directly with ChatGPT.

Meanwhile, the souped-up AI engine will also be powering a number of Meta apps and be integrated into the search engines of Facebook, Instagram, WhatsApp and Messenger.

Meta asserts that the new AI engine will be the best of its kind in the free chatbots arena.

Chatbots like ChatGPT and Google Gemini start at $20/month for consumers.

You can give Zuckerberg’s Meta AI chatbot a free test-drive — but keep in mind that it will take some time before the free tool gets the Llama 3 upgrade.

In other news and analysis on AI writing:

*In-Depth Guide: Flipping the Script: There’s An AI Pivot for Writers Into Video Production: While many writers are understandably concerned that AI could take their jobs, the same tech may be offering a career move for them into video production.

Turns out, writers who can think imagistically are uniquely qualified to use text-to-video tools.

The reason: With each of these tools, the better you are at writing text prompts to trigger the kinds of video you’re imagining:

~ The better you are at getting the video results you want

~The better suited you are to assuming the helm of that video production

This guide offers an in-depth look to Sora, an in-development, text-to-video AI tool from OpenAI that has turned heads all over the film-and-video world and is perceived by many as state-of-the-art.

(You can check-out videos generated by Sora here.)

*Google Gives ChatGPT an Elbow: Gobsmacks With Enhanced Analytical Capability: In more great news for consumers hoping for continued fierce competition amongst AI app providers, Google is also out with an upgrade to its AI.

The upgrade of its flagship AI chatbot has just been rolled-out — and dubbed Gemini Pro 1.5.

The primary new feature of Gemini’s upgrade is its increased ‘context window.’

Essentially, Gemini Pro 1.5 has the ability to analyze up to 700,000 words of data inputted by users — who are looking for Gemini to reference that data as they as ask the chatbot questions, request summaries, engage in brainstorming and enter similar AI prompts regarding the data.

Observes writer Kyle Wiggers: “It’s about four times the amount of data that Anthropic’s flagship model, Claude 3, can take as input — and about eight times as high as OpenAI’s GPT-4 Turbo max context.”

*Bye, Bye Customer Service Telereps — Hello AI: The days when human telereps — who are often poorly trained, underpaid and then thrown to the wolves — may be coming to an end.

Electronics retailer Best Buy has decided to offload most consumer questions about its products to an AI-powered virtual assistant by late Summer 2024.

Sure, at least Best Buy’s human Q&A employees will ostensibly be kept on salary with the move.

But if the AI virtual assistant ends-up answering 90% of callers’ and chatters’ questions in a year’s time, what’s the point of keeping most of those humans employed?

*If Privacy’s Dead, This Pendant is Dancing on Its Grave: From the Department of ‘Privacy — Tell Me Again What That Is?’ a new AI pendant you wear has been released that’s designed to record everything you say and hear — and then analyze those conversations for you later.

Dubbed ‘Limitless Pendant,’ the new tech is ostensibly designed for busy pros looking to record and analyze their work meetings — and then have the pendant transcribe, analyze, make notes and offer summaries of those encounters.

Observes writer David Pierce: “The $99 device is meant to be with you all the time — Siroker says its battery lasts 100 hours — and uses beam-forming tech to more clearly record the person speaking to you and not the rest of the coffee shop or auditorium.

“Everything you record gets uploaded to Limitless, mingled with your other data” and made available through various apps.

*Your Brand’s New Best Friend?: New Marketing Chatbot Powered by AI Neuroscience: The ever-expanding universe of AI tools designed specifically for marketers just got a bit bigger with the release of an AI copilot powered by AI neuroscience.

Observes Thomas Z. Ramsoy, CEO, Neurons: “With Neurons Copilot, users get personalized recommendations to optimize content for higher impact based on industry, platform, channel, and more.

“Copilot acts like an AI creative director when creating and designing marketing and ad campaigns for agencies or products.

“It tells users how to make content more effective, branding more visible, key messages more appealing — and much more.”

*Dreams Of AI Mojo: World’s Largest Ad Agency Partners With Google: In a head-turning move, WPP — parent company of some of the biggest agencies in advertising — has reached-out to Google for AI enhancement.

Specifically, the company is looking to integrate Google’s Gemini AI into its services to help AI-power ad narration, write ad scripts and auto-generate product images.

Observes Stephan Pretorious, Chief Technology Officer, WPP: “I believe this will be a game-changer for our clients and the marketing industry at large.”

*ChatGPT Grabs the Director’s Chair: Its AI Will be Integrated Into Adobe Premiere Pro: You know you’re the cat’s meow when a major company like Adobe opts to integrate your AI into its video production tool.

Specifically, Adobe has decided to add the AI tech behind Sora — an in-developement text-to-video tool from OpenAI, to Adobe Premiere Pro.

Sora is seen by many in the film and video production world as a state-of-the-art — and somewhat scary — AI auto-generation tool for video.

Sora’s ability to create stunning video from just a string of words as input has dazzled many in the video and film industry — and has more than a few worried that it could take their jobs.

Even so, while thunderous in its promise, Sora is still technically an in-development technology and has yet to be released as a stand-alone commercial product.

*Whispering Sweet Nothings: Google Offers New Guide for Prompting AI: Writers and others looking for secrets on how to best prompt Google’s chatbot Gemini now have a new, 40+ page guide they can consult from Google.

Undoubtedly, the guide will be gobbled-up by many regular users of AI chatbots, who are always looking for inside tips on how to put together a string of words — or a prompt — to get precisely what their looking for from the chatbot.

Observes Mike Kaput, Chief Content Officer, Marketing AI Institute: “It’s recommended reading for any professional.”

*Major AI Study Released by Stanford University: Some leading researchers in AI are out with their annual report on the state of the tech.

Some key findings:

~AI beats humans on some tasks, but not on all

~The U.S. leads the world in development of AI engines

~Investment in generative AI like ChatGPT has skyrocketed

~People across the globe are aware of AI’s growing impact — and many are skittish

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post Zuckerberg Looking to Eat ChatGPT’s Lunch appeared first on Robot Writers AI.

Our new electric Atlas platform is here. Supported by decades of visionary robotics innovation and years of practical experience, Boston Dynamics is tackling the next commercial frontier.

Four-legged robots, also known as quadrupedal robots, have advantageous characteristics, including the ability to rapidly walk on challenging terrains and keep a low center of gravity. Some four-legged robots can also manipulate objects in their surroundings, yet this is typically achieved using arm-like structures mounted at the top of the robots, rather than the limbs they use to walk.

Exploring the promise and risks of a future with more capable AI

Exploring the promise and risks of a future with more capable AI

A new robotic suction cup which can grasp rough, curved and heavy stone, has been developed by scientists.

EPFL researchers are targeting the next generation of soft actuators and robots with an elastomer-based ink for 3D printing objects with locally changing mechanical properties, eliminating the need for cumbersome mechanical joints.

Researchers are targeting the next generation of soft actuators and robots with an elastomer-based ink for 3D printing objects with locally changing mechanical properties, eliminating the need for cumbersome mechanical joints.

Maintaining Strategic Interoperability and Flexibility

In the fast-evolving landscape of generative AI, choosing the right components for your AI solution is critical. With the wide variety of available large language models (LLMs), embedding models, and vector databases, it’s essential to navigate through the choices wisely, as your decision will have important implications downstream. 

A particular embedding model might be too slow for your specific application. Your system prompt approach might generate too many tokens, leading to higher costs. There are many similar risks involved, but the one that is often overlooked is obsolescence. 

As more capabilities and tools go online, organizations are required to prioritize interoperability as they look to leverage the latest advancements in the field and discontinue outdated tools. In this environment, designing solutions that allow for seamless integration and evaluation of new components is essential for staying competitive.

Confidence in the reliability and safety of LLMs in production is another critical concern. Implementing measures to mitigate risks such as toxicity, security vulnerabilities, and inappropriate responses is essential for ensuring user trust and compliance with regulatory requirements.

In addition to performance considerations, factors such as licensing, control, and security also influence another choice, between open source and commercial models: 

• Commercial models offer convenience and ease of use, particularly for quick deployment and integration
• Open source models provide greater control and customization options, making them preferable for sensitive data and specialized use cases

With all this in mind, it’s obvious why platforms like HuggingFace are extremely popular among AI builders. They provide access to state-of-the-art models, components, datasets, and tools for AI experimentation. 

A good example is the robust ecosystem of open source embedding models, which have gained popularity for their flexibility and performance across a wide range of languages and tasks. Leaderboards such as the Massive Text Embedding Leaderboard offer valuable insights into the performance of various embedding models, helping users identify the most suitable options for their needs. 

The same can be said about the proliferation of different open source LLMs, like Smaug and DeepSeek, and open source vector databases, like Weaviate and Qdrant.  

With such mind-boggling selection, one of the most effective approaches to choosing the right tools and LLMs for your organization is to immerse yourself in the live environment of these models, experiencing their capabilities firsthand to determine if they align with your objectives before you commit to deploying them. The combination of DataRobot and the immense library of generative AI components at HuggingFace allows you to do just that. 

Let’s dive in and see how you can easily set up endpoints for models, explore and compare LLMs, and securely deploy them, all while enabling robust model monitoring and maintenance capabilities in production.

Simplify LLM Experimentation with DataRobot and HuggingFace

Note that this is a quick overview of the important steps in the process. You can follow the whole process step-by-step in this on-demand webinar by DataRobot and HuggingFace. 

To start, we need to create the necessary model endpoints in HuggingFace and set up a new Use Case in the DataRobot Workbench. Think of Use Cases as an environment that contains all sorts of different artifacts related to that specific project. From datasets and vector databases to LLM Playgrounds for model comparison and related notebooks.

In this instance, we’ve created a use case to experiment with various model endpoints from HuggingFace. 

The use case also contains data (in this example, we used an NVIDIA earnings call transcript as the source), the vector database that we created with an embedding model called from HuggingFace, the LLM Playground where we’ll compare the models, as well as the source notebook that runs the whole solution. 

You can build the use case in a DataRobot Notebook using default code snippets available in DataRobot and HuggingFace, as well by importing and modifying existing Jupyter notebooks. 

Now that you have all of the source documents, the vector database, all of the model endpoints, it’s time to build out the pipelines to compare them in the LLM Playground. 

Traditionally, you could perform the comparison right in the notebook, with outputs showing up in the notebook. But this experience is suboptimal if you want to compare different models and their parameters. 

The LLM Playground is a UI that allows you to run multiple models in parallel, query them, and receive outputs at the same time, while also having the ability to tweak the model settings and further compare the results. Another good example for experimentation is testing out the different embedding models, as they might alter the performance of the solution, based on the language that’s used for prompting and outputs. 

This process obfuscates a lot of the steps that you’d have to perform manually in the notebook to run such complex model comparisons. The Playground also comes with several models by default (Open AI GPT-4, Titan, Bison, etc.), so you could compare your custom models and their performance against these benchmark models.

You can add each HuggingFace endpoint to your notebook with a few lines of code. 

Once the Playground is in place and you’ve added your HuggingFace endpoints, you can go back to the Playground, create a new blueprint, and add each one of your custom HuggingFace models. You can also configure the System Prompt and select the preferred vector database (NVIDIA Financial Data, in this case). 

Figures 6, 7. Adding and Configuring HuggingFace Endpoints in an LLM Playground

After you’ve done this for all of the custom models deployed in HuggingFace, you can properly start comparing them.

Go to the Comparison menu in the Playground and select the models that you want to compare. In this case, we’re comparing two custom models served via HuggingFace endpoints with a default Open AI GPT-3.5 Turbo model.

Note that we didn’t specify the vector database for one of the models to compare the model’s performance against its RAG counterpart. You can then start prompting the models and compare their outputs in real time.

There are tons of settings and iterations that you can add to any of your experiments using the Playground, including Temperature, maximum limit of completion tokens, and more. You can immediately see that the non-RAG model that doesn’t have access to the NVIDIA Financial data vector database provides a different response that is also incorrect. 

Once you’re done experimenting, you can register the selected model in the AI Console, which is the hub for all of your model deployments. 

The lineage of the model starts as soon as it’s registered, tracking when it was built, for which purpose, and who built it. Immediately, within the Console, you can also start tracking out-of-the-box metrics to monitor the performance and add custom metrics, relevant to your specific use case. 

For example, Groundedness might be an important long-term metric that allows you to understand how well the context that you provide (your source documents) fits the model (what percentage of your source documents is used to generate the answer). This allows you to understand whether you’re using actual / relevant information in your solution and update it if necessary.

With that, you’re also tracking the whole pipeline, for each question and answer, including the context retrieved and passed on as the output of the model. This also includes the source document that each specific answer came from.

How to Choose the Right LLM for Your Use Case

Overall, the process of testing LLMs and figuring out which ones are the right fit for your use case is a multifaceted endeavor that requires careful consideration of various factors. A variety of settings can be applied to each LLM to drastically change its performance. 

This underscores the importance of experimentation and continuous iteration that allows to ensure the robustness and high effectiveness of deployed solutions. Only by comprehensively testing models against real-world scenarios, users can identify potential limitations and areas for improvement before the solution is live in production.

A robust framework that combines live interactions, backend configurations, and thorough monitoring is required to maximize the effectiveness and reliability of generative AI solutions, ensuring they deliver accurate and relevant responses to user queries.

By combining the versatile library of generative AI components in HuggingFace with an integrated approach to model experimentation and deployment in DataRobot organizations can quickly iterate and deliver production-grade generative AI solutions ready for the real world.

The post How to Choose the Right LLM for Your Use Case appeared first on DataRobot AI Platform.

A new robotic suction cup that can grasp rough, curved and heavy stone, has been developed by scientists at the University of Bristol. The team, based at Bristol Robotics Laboratory, studied the structures of octopus biological suckers, which have superb adaptive suction abilities enabling them to anchor to rock.

A team of roboticists at the University of California, Berkeley, reports that it is possible to train robots to do relatively simple tasks by using sim-to-real reinforcement learning to train them. In their study, published in the journal Science Robotics, the group trained a robot to walk in unfamiliar environments while it carried different loads, all without toppling over.

A team of roboticists and mechanical and aeronautical engineers at Stanford University has developed a spider-like robot for possible use in exploring caves or lava tubes on Mars. In their paper published in the journal Science Robotics, the group describes their reasons for developing the new robot, their inspiration for the design, and how well it worked when tested in a real-world environment.