A yet another milestone in the history of A.I. and Robotics!
Yes, I’m not exaggerating! What you could potentially read in a moment would be a futuristic world where humanoid robots can very well serve humanity in many ways (keeping negatives out of the picture for timebeing).
When I first heard this news, movies such as I, Robot and Enthiran, the Robot were flashing on my mind! Putting my filmy fantasies aside, the Robotics expert company Figure, in partnership with Microsoft and OpenAI, has released the first general purpose humanoid robot – Figure 01 – designed for commercial use.
Here’s the quick video released by the creators –
Figure’s Robotics expertise has been perfectly augmented by OpenAI’s multi-modal support in understanding and generating response of visual inputs such as image, audio, video. The future looks way more promising and becoming reality that these humanoids can be supplied to the manufacturing and commercial areas where there are shortage of resources for scaling the production needs.
In the video, it is seen demonstrating the ability to recognize objects such as apple and take appropriate actions. It is reported that Figure 01 humanoid robot stands at 5 feet 6 inches tall and weighs 132 pounds. It can carry up to 44 pounds and move at a speed of 1.2 meters per second.
Figure is backed by tech giants such as Microsoft, OpenAI Startup Fund, NVIDIA, Jeff Bezos (Bezos Expeditions) and more.
Lot of fascinating innovations happening around us thanks to Gen AI / LLMs, Copilot, Devin, Sora, and now a glimpse into the reality of Humanoid Robotics. Isn’t it a great time to be in?!
Gen AI enables writing highly sophisticated code for the given problem statement. Developers can already take advantage of that!
What if a full-fledged tool that can write code, fix bugs, leverages online resources, collaborates with human, and solves gigs on popular freelancing sites such as Upwork?!
Is this a fiction? Well, not anymore.
Meet Devin, the first of its kind, AI-based software engineer, created by Cognition Labs, an applied AI labs company that builds apps focusing on reasoning.
The Tech World is already amazed with the capabilities of Copilot which assists in developing code snippets, however, Devin has a unique capability and is a step-up in terms of its features that it can cater to end-to-end software development.
According to the creators, Devin has the following key capabilities as of writing –
Learn how to use unfamiliar technologies.
Build and deploy apps end to end.
Autonomously find and fix bugs in codebases.
Train and fine tune its own AI models.
Address bugs and feature requests in open source repositories.
Contribute to mature production repositories.
Solve real jobs on Upwork!
Scott Wu, the founder and CEO of Cognition, explained Devin can access common developer tools, including its own shell, code editor and browser, within a sandboxed compute environment to plan and execute complex engineering tasks requiring thousands of decisions.
Devin resolved 13.86% of issues without human assistance in the tested GitHub repositories as per the publication by creators based on SWE-benchmark that asks agents to resolve challenging problems in the open-source projects such as scikit-learn, Django.
There’s sparkling conversation around the globe that AI could kill basic coding skills written by human and recently NVidia Founder talked about everyone is now a programmer thanks to AI. Of course, I think, human oversight is required to refine and meet user’s requirements.
Thanks to Devin, now the human can focus more on complex or interesting problems that requires our creativity and best use of our time. As of now, access to Devin is only limited to select individuals. Public access is still pending. For more info, visit cognition-labs.com/blog
Meta, the parent company of Facebook, unveiled the latest version of LLaMa 2 for research and commercial purposes. It’s released as open-source unlike OpenAI GPT / Google Bard which is proprietary.
What is LLaMa?
LLaMa (Large Language Model Meta AI) is an open-source language model built by Meta’s GenAI team for research. LLaMa 2 which is newly released for research and commercial uses.
Difference between LLaMa and LLaMa 2
LLaMa 2 model was trained on 40% more data than its predecessor. Al-Dahle (vice president at Meta who is leading the company’s generative AI work) says there were two sources of training data: data that was scraped online, and a data set fine-tuned and tweaked according to feedback from human annotators to behave in a more desirable way. The company says it did not use Meta user data in LLaMA 2, and excluded data from sites it knew had lots of personal information.
Newly released LLaMa 2 models will not only further accelerate the LLM research work but also enable enterprises to build their own generative AI applications. LLaMa 2 includes 7B, 13B and 70B models, trained on more tokens than LLaMA, as well as the fine-tuned variants for instruction-following and chat.
According to Meta, its LLaMa 2 “pretrained” models are trained on 2 trillion tokens and have a context window of 4,096 tokens (fragments of words). The context window determines the length of the content the model can process at once. Meta also says that the LLaMa 2 fine-tuned models, developed for chat applications similar to ChatGPT, have been trained on “over 1 million human annotations.”
Databricks highlights the salient features of such open-source LLMs:
No vendor lock-in or forced deprecation schedule
Ability to fine-tune with enterprise data, while retaining full access to the trained model
Model behavior does not change over time
Ability to serve a private model instance inside of trusted infrastructure
Tight control over correctness, bias, and performance of generative AI applications
Microsoft says that LLaMa 2 is the latest addition to their growing Azure AI model catalog. The model catalog, currently in public preview, serves as a hub of foundation models and empowers developers and machine learning (ML) professionals to easily discover, evaluate, customize and deploy pre-built large AI models at scale.
OpenAI GPT vs LLaMa
A powerful open-source model like LLaMA 2 poses a considerable threat to OpenAI, says Percy Liang, director of Stanford’s Center for Research on Foundation Models. Liang was part of the team of researchers who developed Alpaca, an open-source competitor to GPT-3, an earlier version of OpenAI’s language model.
“LLaMA 2 isn’t GPT-4,” says Liang. Compared to closed-source models such as GPT-4 and PaLM-2, Meta itself speaks of “a large gap in performance”. However, ChatGPT’s GPT-3.5 level should be reached by Llama-2 in most cases. And, Liang says, for many use cases, you don’t need GPT-4.
A more customizable and transparent model, such as LLaMA 2, mighthelp companies create products and services faster than a big, sophisticated proprietary model, he says.
“To have LLaMA 2 become the leading open-source alternative to OpenAI would be a huge win for Meta,” says Steve Weber, a professor at the University of California, Berkeley.
LLaMA 2 also has the same problems that plague all large language models: a propensity to produce falsehoods and offensive language. The fact that LLaMA 2 is an open-source model will also allow external researchers and developers to probe it for security flaws, which will make it safer than proprietary models, Al-Dahle says.
With that said, Meta has set to make its presence felt in the open-source AI space as it has announced the release of the commercial version of its AI model LLaMa. The model will be available for fine-tuning on AWS, Azure and Hugging Face’s AI model hosting platform in pretrained form. And it’ll be easier to run, Meta says — optimized for Windows thanks to an expanded partnership with Microsoft as well as smartphones and PCs packing Qualcomm’s Snapdragon system-on-chip. The key advantage of on-device AI is cost reduction (cloud per-query costs) and data security (as data solely remain on-device)
LLaMa can turn out to be a great alternative for pricy proprietary models sold by OpenAI like ChatGPT and Google Bard.
Generative AI is the new buzzword since late 2022. The likes of ChatGPT, Bard, etc. is taking the AI to the all new levels with wide variety of use-cases for consumers and enterprises.
I wanted to briefly understand the difference between traditional AI and generative AI. According to a recent report published in Deloitte, GenAI’s output is of a higher complexity while compared with traditional AI.
Typical AI models would generate output in the form of a value (Ex: predicting sales for next quarter), label (Ex: classifying a transaction as legitimate or fraud). GenAI models tend to generate a full page of composed text or other digital artifact. Applications like Midjourney, DALL-E produces images, for instance.
In the case of GenAI, there is no one possible correct answer. Deloitte study reports, this results in a large degree of freedom and variability, which can be interpreted as creativity.
The underlying GenAI models are usually large in terms of resources consumption, requiring TBs of high-quality data processed on large-scale, GPU-enabled, high-performance computing clusters. With OpenAI’s innovation being plugged into Microsoft Azure Services and Office suites, it would be interesting to see the dramatic changes in consumers’ productivity!
It appears as if the movie – Terminator – was released quite recently and many of us have talked about if machines could help us in our daily chore activities and supporting business operations.
Fast forward! We’re already realizing few changes around us where artificial intelligence enabled systems help us in many ways and the potential of it looks bright few years down the road.
I was researching about few top use cases of AI in business a couple of weeks. I thought to share it here and I’m sure you’re going to be excited to read & share.
Top Use Cases of AI in Business
1. Computer Vision – Smart Cars (Autonomous Cars): IBM survey results say 74% expected that we would see smart cars on the road by 2025. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically based on the driver, report and even fix problems itself, drive itself, and offer real time advice about traffic and road conditions.
2. Robotics: In 2010, Japan’s SoftBank telecom operations partnered with French robotic manufacturer Aldebaran to develop Pepper, a humanoid robot that can interact with customers and “perceive human emotions.” Pepper is already popular in Japan, where it’s used as a customer service greeter and representative in 140 SoftBank mobile stores.
3. Amazon Drones: In July 2016, Amazon announced its partnership with the UK government in making small parcel delivery via drones a reality. The company is working with aviation agencies around the world to figure out how to implement its technology within the regulations set forth by said agencies. Amazon’s “Prime Air” is described as a future delivery system for safely transporting and delivering up to 5-pound packages in less than 30 minutes.
4. Augmented Reality:Ex: Google Glass: It can show the location of items you are shopping for, with information such as cost, nutrition, or if another store has it for less money. Being AI it will understand that you’re likely to ask for the weather at a certain time, or want reminders about meetings so it will simply “pop up” unobtrusively.
5. Marketing Personalization: Companies can personalize which emails a customer receives, which direct mailings or coupons, which offers they see, which products show up as “recommended” and so on, all designed to lead the consumer more reliably towards a sale. You’re probably familiar with this use if you use services like Amazon or Netflix. Intelligent machine learning algorithms analyze your activity and compare it to the millions of other users to determine what you might like to buy or binge watch next.
6. Chatbots: Customers want the convenience of not having to wait for a human agent to handle their call, or wait for a few hours to be replied to an email/twitter query. Chatbots are instant, 24×7 available & backed by robust AI offering Contextually relevant personalized conversation.
7. Fraud Detection: Machine learning is getting better and better at spotting potential cases of fraud across many different fields. PayPal, for example, is using machine learning to fight money laundering.
8. Personal Security: Airports – AI can spot things human screeners might miss in security screenings at airports, stadiums, concerts, and other venues. That can speed up the process significantly and ensure safer events.
9. Healthcare: Machine learning algorithms can process more information and spot more patterns than their human counterparts. One study used computer assisted diagnosis (CAD) when to review the early mammography scans of women who later developed breast cancer, and the computer spotted 52% of the cancers as much as a year before the women were officially diagnosed.
1. Computer Vision – Smart Cars (Autonomous Cars): IBM survey results say 74% expected that we would see smart cars on the road by 2025. It might adjust the internal settings — temperature, audio, seat position, etc. — automatically based on the driver, report and even fix problems itself, drive itself, and offer real time advice about traffic and road conditions.