IBL News

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  • Hugging Face Unveiled Two Open-Source Humanoid Robots

    Hugging Face Unveiled Two Open-Source Humanoid Robots

    IBL News | New York

    AI development platform Hugging Face released two open-source robots, HopeJr and Reachy Mini. The first few units are expected to be shipped by the end of the year.

    HopeJR is a humanoid robot capable of walking, moving its arms, and manipulating many objects, with 66 independent movements. It costs under $3,000. 

    Reachy Mini is a desktop unit that can move its head, talk, listen, and be used to test AI apps. The cost will be around $300.

    “The important aspect is that these robots are open source, so anyone can assemble, rebuild, and understand how they work, and that they’re affordable, so that robotics doesn’t get dominated by just a few big players with dangerous black-box systems,” the company’s co-founder and CEO, Clem Delangue, told TechCrunch.

    This robot release was made possible in part by the company’s acquisition of humanoid robotics startup Pollen Robotics, which was announced in April.

    UPDATE JUL 9, 2025:
    Hugging Face said it plans to release two versions of the Reachy Mini. The first, Reachy Mini Wireless, will cost $449 and run a Raspberry Pi 5 mini computer. The second version is the Reachy Mini Lite, which requires a connection to a computing source and is priced at $299.

    The open-source robots are fully programmable in Python and come with a set of pre-installed demos, as well as integration with the Hugging Face Hub, which provides users with access to more than 1.7 million AI models and over 400,000 datasets.

  • A Y Combinator Startup Launches an On-Demand CTO and Founding Engineer

    A Y Combinator Startup Launches an On-Demand CTO and Founding Engineer

    IBL News | New York

    Emergent, a Y Combinator-backed company, launched a virtual, on-demand CTO engineer this month that develops production-ready apps with backends, databases, and integrations.

    According to the company, this new vibe coding tool goes beyond agent prototypes and mockups, enabling the production of “full-stack applications with stunning UI and real backend, with no developers required.”

    At its core, Emergent is an integrated platform that autonomously builds, tests, and ships software. The user describes his product in plain language, and the platform handles the architecture, logic, and implementation.

    Emergent aims to democratize access to advanced software creation, as AI can truly understand what’s needed and build complete solutions.

    The start-up claimed that in two weeks, users have built over 10,000 apps—these range from landing pages to SaaS tools like AI notetakers, Slack bots, and Figma plugins.

    Emergent was founded by twin brothers, Indian-born and US-educated engineers, Mukund Jha (CEO) and Madhav Jha (CTO).

  • Anthropic Develops a New Tool to Automate Prompt Engineering

    Anthropic Develops a New Tool to Automate Prompt Engineering

    IBL News | New York

    Anthropic released a new feature on its Claude 3.5 Sonnet console this month, which allows prompts to be generated, tested, and evaluated. It takes a short task description and constructs a much longer, fleshed-out prompt, utilizing Anthropic’s prompt engineering techniques.

    The Claude console offers a built-in prompt generator powered by Claude 3.5 Sonnet. This generator allows users to describe their tasks and generate a high-quality prompt.

    The test case generation feature allows the creation of input variables for the prompt. This means that developers can compare the effectiveness of various prompts side-by-side and rate sample answers on a five-point scale.

    The Anthropic Console is a test kitchen for developers, created to attract businesses looking to build products with Claude.

    It can save developers time and effort, especially with little or no prompt engineering experience.

    Crafting high-quality prompts is challenging. It requires deep knowledge of your application’s needs and expertise with large language models. Prompt engineering has become a highly demanded job in the AI industry.

    “Prompt engineering is one of the most important things for widespread enterprise adoption of generative AI; it sounds simple, but 30 minutes with a prompt engineer can often make an application work when it wasn’t before,” said Anthropic CEO and co-founder Dario Amodei.

  • Google Released ‘Lumiere’, Which Utilizes Unique Architecture to Generate AI Video

    Google Released ‘Lumiere’, Which Utilizes Unique Architecture to Generate AI Video

    IBL News | New York

    Google introduced this week Lumiere, a text-to-video generation AI model designed for to portray realistic clips. It’s one of the most advanced text-to-video generators yet demonstrated, although it is still in a primitive state.

    Existing AI video models synthesize keyframes followed by temporal super-resolution. But Google uses a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model.

    “We demonstrate state-of-the-art text-to-video generation results, and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation,” said the company.

    Lumiere does a good job of creating videos of cute animals in ridiculous scenarios, such as using roller skates, driving a car, or playing a piano. It’s worth noting that AI companies often demonstrate video generators with cute animals because generating coherent, non-deformed humans is currently difficult.

    As for training data, Google doesn’t say where it got the videos it fed into Lumiere, writing, “We train our T2V [text to video] model on a dataset containing 30M videos along with their text caption. [sic] The videos are 80 frames long at 16 fps (5 seconds). The base model is trained at 128×128.”

    Other video generators are Meta’s Make-A-Video, Runway’s Gen2, and Stable Video Diffusion, which can generate short clips from still images.
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  • Google Announced AI-Powered Features for Classroom Management

    Google Announced AI-Powered Features for Classroom Management

    IBL News | New York

    Google announced AI-powered features for classroom management, questions, and lesson plan creation, as well as other functionalities.

    Teachers will be able to add AI-suggested questions to YouTube videos as part of their Classroom assignment.

    The Practice sets feature, which uses AI to create answers and general hints, is now available in over 50 languages. Plus, educators can turn a Google Form into a practice set.

    Additionally, Google is introducing a new Resources tab to manage practice sets and interactive questions asked during a video.

    Google’s generative AI tool for Google Workspace, Duet AI, can assist teachers in coming up with a lesson plan.

     

    Teachers will be able to use the speaker spotlight feature in Slides to create a lesson with narration along with the slide deck.

    The company is updating Classroom analytics so educators can look at stats like assignment completion and trends for grades.

    Google is adding the ability to get text from PDFs for screen readers on ChromeOS.

  • LangChain announced LangSmith, a System for Evaluating and Monitoring LLM Applications

    LangChain announced LangSmith, a System for Evaluating and Monitoring LLM Applications

    IBL News | New York

    LangChain announced LangSmith, a unified platform, now in closed beta, for debugging, testing, evaluating, and monitoring Large Language Model (LLM) – powered applications this week.

    “LangSmith is a platform to help developers close the gap between prototype and production; it’s designed for building and iterating on products that can harness the power–and wrangle the complexity–of LLMs,” said the company.“LangChain has been instrumental in helping us prototype intelligent agents at Snowflake,” said Adrien Treuille, Director of Product at Snowflake.

    “LangSmith was easy to integrate, and the agnostic open source API made it very flexible to adapt to our implementation,” tacked on Richard Meng, Senior Software Engineer at Snowflake.

    Boston Consulting Group also built a highly customized and highly performant series of applications on top of LangChain’s framework by relying on this same infrastructure.
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  • McKinsey Selected Startup Cohere to Provide Clients Generative AI Solutions

    McKinsey Selected Startup Cohere to Provide Clients Generative AI Solutions

    IBL News | New York

    Consultancy giant McKinsey picked up neutral model provider, Cohere as a partner to sell AI-based customer engagement and workflow automation solutions to its enterprise clients.

    Cohere competes with OpenAI, Google, and Microsoft, with a focus on generative AI solutions for enterprises.

    McKinsey is also considering using Cohere to increase its internal efficiency and power the knowledge management system at the company.

    “We are seeing our clients consider cost, IP protection, and consumer privacy, and how the model is trained. We found Cohere to be one of the great solutions out there,” Ben Ellencweig, senior partner at McKinsey, told Reuters.

    Last month, Cohere raised $270 million from investors, including Nvidia, Oracle, and Salesforce Ventures, at a $2.2 billion valuation.

    It announced a partnership with Oracle, which will embed Cohere’s generative AI technology in its products.

    Bain and Company has teamed up with OpenAI, while Deloitte has partnered with Nvidia.
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  • ChatGPT’s iOS and Android App Can Now Search the Web

    ChatGPT’s iOS and Android App Can Now Search the Web

    IBL News | New York

    OpenAI, this week, announced that Plus users on the mobile iOS and Android ChatGPT app can now use Browsing for queries relating to current events and other information that extend original training data, that is, the year 2021.

    This feature can be enabled by selecting “GPT-4” and choosing “Browser with Bing.”

    The fact that Bing is the only search engine available draws critics, as the users don’t have any alternatives to choose from.

    Techcrunch wrote: “Limiting ChatGPT’s search capabilities to Bing seems just short of a user-hostile move. The business motivations are obvious — OpenAI has a close partnership with Microsoft, which has invested over $10 billion in the startup — but Bing is far from the be-all and end-all of search engines.”

    Recently, a Stanford study showed evidence that Bing’s top search results contained an “alarming” amount of disinformation.

    On the other hand, also this week, OpenAI’s CEO Sam Altman told some developers that his company wants to turn it into a “supersmart personal assistant for work.”

    With built-in knowledge about an individual and their workplace, this assistant could carry out tasks such as drafting emails or documents in that person’s style and with up-to-date information about their business.

    The assistant features could put OpenAI on a collision course with Microsoft, its primary business partner, investor, and cloud provider, as well as with other OpenAI software customers such as Salesforce.

    Those firms also want to use OpenAI’s software to build AI “copilots” for people to use at work.
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  • What Are the Most Important Learning Analytics?

    What Are the Most Important Learning Analytics?

    IBL News & IBL Education | New York

    There are many important learning analytics, but some of the most important ones include completion rates, time on task, engagement levels, achievement rates, and the use of learning resources. These metrics can provide valuable insights into how well students are learning and how effective a given teaching method or learning environment is. By tracking these metrics, educators can identify areas for improvement and make more informed decisions about how to best support student learning.

    Other important learning analytics might include:

    – Student progress over time: This metric can help educators understand how well students are progressing in their learning, and whether they are making the expected amount of progress given their starting point.

    – Student feedback: Gathering and analyzing student feedback can provide valuable insights into how students perceive their learning experience, and can help identify areas where students are struggling or where the learning environment is not meeting their needs.

    – Learner demographics: Understanding the demographics of the students in a given class or program can help educators tailor their teaching approach and learning materials to better meet the needs of their students.

    – Learner behavior: Analyzing how students interact with learning materials and resources can provide valuable insights into how they approach learning and what strategies are most effective for them.

    – Learning outcomes: Tracking learning outcomes can help educators understand the effectiveness of their teaching methods and the overall quality of the learning experience. By comparing learning outcomes across different classes or programs, educators can identify best practices and make more informed decisions about how to improve student learning.

    What’s the best way to track learner feedback?

    One of the best ways to track learner feedback is to use surveys or other tools that allow students to provide their opinions and experiences with the learning environment. Surveys can be administered regularly (e.g., at the end of each unit or course) to gather ongoing feedback from students. Surveys can be designed to ask specific questions about different aspects of the learning experience, such as the quality of the materials, the effectiveness of the teaching methods, and the overall satisfaction with the learning environment.

    A SERIES OF ARTICLES ABOUT ‘AI, CLOUD, AND ADVANCED TECHNOLOGIES IN EDUCATION’ WRITTEN BY THE IBL AI ENGINE IN DECEMBER 2022

     

  • March 2020

    March 2020

    MARCH 1-4, 2020, Seattle, WA
    Innovations Conference

    MARCH 2-4, 2020, Bellevue, WA
    ELI

    MARCH 9-12, 2020, Austin, TX
    SXSW EDU

    MARCH 11-13, 2020, Tempe, Arizona
    ShapingEDU

    MARCH 12-15, Orlando, FL
    ICTM – International Conference on Technology in Collegiate Mathematics

    MARCH 13-14, London, UK
    International Transform MedEd Conference 2020

    MARCH 14-17, 2020, San Diego, CA
    ACE 2020

    MARCH 16-20, 2020, San Francisco, CA
    Game Developers Conference

    MARCH 18-20, 2020, Boston, MA
    UPCEA 2020 Annual Conference

    MARCH 22-26, 2020, San Jose, CA
    GTC 2020

    MARCH 23-26, 2020, Austin, TX
    Corporate Learning Week

    MARCH 28-APRIL 1, 2020, Austin, TX
    2020 NASPA Annual Conference

    MARCH 29, 2020, San Diego, CA
    EdSurge Immersion San Diego 2020

    MARCH 30 – APRIL 1, 2020, San Diego, CA
    ASU GSV

    MARCH 31 – APRIL 3, 2020, Chicago, IL
    OLC Innovate