Monthly Archives: September 2022

PALM SPRINGS, Calif.--(BUSINESS WIRE)--The worlds top thinkers and builders in the artificial intelligence industry will gather at the inaugural Edge of Now (EON) event taking place October 19-21 in Palm Springs, leading in-depth discussions on the opportunities and challenges of deploying machine perception at scale.

EON has announced its key speaker lineup, which includes AI practitioners from companies such as Toyota, Sony, and J.P. Morgan Chase, as well as senior technologists from leading AI companies Parallel Domain and Berkshire Grey. EON speakers are also professors teaching and researching the next generation of AI technologies at Stanford University, The University of Texas at Austin, and Carnegie Mellon University. At EON, hear from:

A unique invitation-only gathering, EON will convene an intimate group of AI early adopters to share best practices for using AI to solve real-world problems. The brightest minds building, deploying, and investing in applied AI today will gather in an unconference setting, creating a participant-driven learning and networking forum. More speakers will be announced in the coming weeks.

Apply for an invitation to attend EON

Inquire about EON sponsorship opportunities

About EON

Edge of Now (EON) is not just an annual event, but a trusted community of multidisciplinary innovators who connect throughout the year to advance the deployment of AI and support the development of next-generation automation technologies. By gathering both the builders and implementers of AI, the EON community finds solutions to real-world problems. For more information, visit http://www.eon.events.

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Edge of Now Announces Illustrious Speaker Lineup of top AI Thinkers and Builders - Business Wire

Image Source: FreeImages

NOTE: This entire article was generated by an Artificial Intelligence website that will write a blog post for you with only an idea as an input. The only thing I did to produce these words was tell it to write a blog about Rockford Illinois. Here's what it gave me. If I personally make any additional comments I will put it in brackets and make it bold. You can try out the program, it's free, at writesonic.com.

Today, in almost every industry, the role of artificial intelligence is growing. AI programs are being used for everything from stock trading to customer service and even marketing. This is because AI is a great way to analyze large data sets and understand trends in that data. For example, an AI bot can read thousands of articles about a company and learn which words are most commonly used when describing them. This got me thinking what could you do if you had access to an AI program? So I decided to put this question to the test by having it write about my hometown: Rockford, IL. Heres what happened

Rockford is a city in the northern part of Illinois. It has a population of about 150,000 people, the majority of whom are Caucasian. Its located about 80 miles from Chicago, making it a commuter town for people living in the city. This means that its a fairly quiet place, with a nice suburban setting thats perfect for families. Rockford has a couple of major industries healthcare, manufacturing, and education (most notably, its home to Northern Illinois University). The economy, in general, is pretty good, with a low unemployment rate.

One of the most interesting things about AI is the way it learns. When you train a program to do something, its going to pick up on what humans are doing and try to emulate that. And its amazing how well it can imitate us not only in what it does, but also in the way it talks. Its like having an infinite database at your fingertips for writing content. If youre having trouble getting started, or you just cant think of a topic, you can ask an AI program to help you out. Heres the thing, though: AI programs cant write on their own. They need human assistance to create their articles. This means that, if you want to get an AI program to write about something, youre going to have to give it a bit of direction.

I decided to ask an AI program to write about Rockford, IL. I used Wordsmith, which is one of the most popular AI writing programs out there. Heres the experiment: I told Wordsmith to write about Rockford, IL. No other direction. Then, I wrote the article it generated based on its content. Heres the article that was written: Its a sunny day in the city of Rockford, and the weather is lovely. There are many birds chirping near my window and a couple of kids playing with their soccer ball in the garden outside my house. My laptop is in front of me, and I am excited to write about this lovely place. Rockford is a city in the northern part of Illinois, United States. It is situated on the Rock River and has a population of about 150,000 people. It is a nice city and a nice place to live. The cost of living here is low, and the students get a good education in one of the many schools of this city. This is a wonderful place to live and work, and many people do.

With that in mind, lets look at the article the AI bot generated. Its a sunny day in the city of Rockford, and the weather is lovely. There are many birds chirping near my window and a couple of kids playing with their soccer ball in the garden outside my house. My laptop is in front of me, and I am excited to write about this lovely place. Rockford is a city in the northern part of Illinois, United States. It is situated on the Rock River and has a population of about 150,000 people. It is a nice city and a nice place to live. The cost of living here is low, and the students get a good education in one of the many schools of this city. This is a wonderful place to live and work, and many people do.

Its fascinating to see how an AI program works. When you ask one to write about a city, itll pick up on what people like about it and what makes it special. Itll also use common words and phrases that are used when describing that place. Its like having an infinite database at your fingertips for writing content. If youre having trouble getting started, or you just cant think of a topic, you can ask an AI program to help you out. With a bit of guidance, you can generate some great content for your blog or website.

Rockford has some weird intersections as well as "worst" intersections. Test your knowledge and see if you can correctly name all 20 of these interactions in the forest city.

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The Day I Asked An AI Program To Write About Rockford. - 1440wrok.com

Humanity is facing many challenges, from human rights to pandemics to climate change, and AI can be our best tool against them, says Alexandr Wang, CEO and founder of Scale AI. Thats what we dedicated Scale to: How do we enable the most ambitious organizations in the world to utilize artificial intelligence to solve the most transformational problems today?

On this weeks Most Innovative Companies podcast, Wang explains how Scale is making data useful in order to help not only tackle our biggest problems, but also unleash human creativity. He talks about why modern AI systems are no match for humans and never will be, how to foster innovation as a company scales in size, and why he believes that optimism can shape reality.

Over the last few months, Scale has been working closely with the U.S. and Ukrainian governments to better understand whats happening in the region. Weve used satellite data and ran artificial intelligence algorithms on top of that data to map out all of the major cities: Kyiv, Kharkiv, Dnipro, Mariupol, Wang says. And then we mapped out, on a day-by-day basis, the level of damage to every single structure within these citiesliterally building by building.

This effort is enabling both governments to divert humanitarian and medical resources where theyre needed most, answering questions such as what is the level of damage that is being caused by the war with Russia, how can we divert infrastructure projects, and how can we divert our resources to immediately address wherever theres meaningful damage on a day by day basis.

Scales work is not limited to the Ukraine conflict, though. Wang cites several use cases among many in the application of modern AI and data labeling technology. In medicine, for example, Scale is working to streamline access to healthcare by facilitating the automatic detection of illness. In the wake of global doctor shortages, medical facilities simply cant train people fast enough, he explains, but AI can lighten the load.

Enamored by data, Wang dropped out of MIT at age 19 to found Scale. Today, at just 25, he is the worlds youngest self-made billionaire. With 700 employees and growing, Scale has managed to get big without going bad, becoming, as Wang describes it, something like a nuclear reactor for energy and excitement.

Wang believes that ambition shapes reality, describing a phenomenon by which people tend to accomplish the magnitude of their ambition.

This is one of the reasons why, as he was growing Scale, Wang always hired people who give a shit: Its impossible to build something and nearly impossible to make magic if nobody around you cares about what theyre doing, he says. Which is why, he adds, its crucial to create a culture where people can dream big and have a sense of optimism around what theyre able to accomplish: Theres nothing more seductive than the invitation to think bigger.

Retaining the sort of energy and drive for which startups are fondly known is also key. [That environment] is one of the most powerful forces in the world, frankly. And that only happens if you get the preconditions just right, where people feel safe, people feel excited, people are excited about what theyre doing.

When you get the conditions for innovation right, Wang says, you know it when you see it. You just need to keep trying to find it. And once you find it, never let it go.

Listen to the episode for the full interview.

You can listen and subscribe to Most Innovative Companies on Apple Podcasts, Stitcher, Spotify, or wherever you get your podcasts.

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Can AI solve the world's biggest problems? - Fast Company

An art prize at the Colorado State Fair was awarded last month to a work that unbeknown to the judges was generated by an artificial intelligence (AI) system.

Social media have also seen an explosion of weird images generated by AI from text descriptions, such as the face of a shiba inu blended into the side of a loaf of bread on a kitchen bench, digital art.

Or perhaps A sea otter in the style of Girl with a Pearl Earring by Johannes Vermeer:

You may be wondering whats going on here. As somebody who researches creative collaborations between humans and AI, I can tell you that behind the headlines and memes a fundamental revolution is under way with profound social, artistic, economic and technological implications.

You could say this revolution began in June 2020, when a company called OpenAI achieved a big breakthrough in AI with the creation of GPT-3, a system that can process and generate language in much more complex ways than earlier efforts. You can have conversations with it about any topic, ask it to write a research article or a story, summarise text, write a joke, and do almost any imaginable language task.

Read more: Robots are creating images and telling jokes. 5 things to know about foundation models and the next generation of AI

In 2021, some of GPT-3s developers turned their hand to images. They trained a model on billions of pairs of images and text descriptions, then used it to generate new images from new descriptions. They called this system DALL-E, and in July 2022 they released a much-improved new version, DALL-E 2.

Like GPT-3, DALL-E 2 was a major breakthrough. It can generate highly detailed images from free-form text inputs, including information about style and other abstract concepts.

For example, here I asked it to illustrate the phrase Mind in Bloom combining the styles of Salvador Dal, Henri Matisse and Brett Whiteley.

Since the launch of DALL-E 2, a few competitors have emerged. One is the free-to-use but lower-quality DALL-E Mini (developed independently and now renamed Craiyon), which was a popular source of meme content.

Around the same time, a smaller company called Midjourney released a model that more closely matched DALL-E 2s capabilities. Though still a little less capable than DALL-E 2, Midjourney has lent itself to interesting artistic explorations. It was with Midjourney that Jason Allen generated the artwork that won the Colorado State Art Fair competition.

Google too has a text-to-image model, called Imagen, which supposedly produces much better results than DALL-E and others. However, Imagen has not yet been released for wider use so it is difficult to evaluate Googles claims.

In July 2022, OpenAI began to capitalise on the interest in DALL-E, announcing that 1 million users would be given access on a pay-to-use basis.

However, in August 2022 a new contender arrived: Stable Diffusion.

Stable Diffusion not only rivals DALL-E 2 in its capabilities, but more importantly it is open source. Anyone can use, adapt and tweak the code as they like.

Already, in the weeks since Stable Diffusions release, people have been pushing the code to the limits of what it can do.

To take one example: people quickly realised that, because a video is a sequence of images, they could tweak Stable Diffusions code to generate video from text.

Another fascinating tool built with Stable Diffusions code is Diffuse the Rest, which lets you draw a simple sketch, provide a text prompt, and generate an image from it. In the video below, I generated a detailed photo of a flower from a very rough sketch.

In a more complicated example below, I am starting to build software that lets you draw with your body, then use Stable Diffusion to turn it into a painting or photo.

What does it mean that you can generate any sort of visual content, image or video, with a few lines of text and a click of a button? What about when you can generate a movie script with GPT-3 and a movie animation with DALL-E 2?

And looking further forward, what will it mean when social media algorithms not only curate content for your feed, but generate it? What about when this trend meets the metaverse in a few years, and virtual reality worlds are generated in real time, just for you?

These are all important questions to consider.

Some speculate that, in the short term, this means human creativity and art are deeply threatened.

Perhaps in a world where anyone can generate any images, graphic designers as we know them today will be redundant. However, history shows human creativity finds a way. The electronic synthesiser did not kill music, and photography did not kill painting. Instead, they catalysed new art forms.

I believe something similar will happen with AI generation. People are experimenting with including models like Stable Diffusion as a part of their creative process.

Or using DALL-E 2 to generate fashion-design prototypes:

A new type of artist is even emerging in what some call promptology, or prompt engineering. The art is not in crafting pixels by hand, but in crafting the words that prompt the computer to generate the image: a kind of AI whispering.

The impacts of AI technologies will be multidimensional: we cannot reduce them to good or bad on a single axis.

New artforms will arise, as will new avenues for creative expression. However, I believe there are risks as well.

Read more: So this is how it feels when the robots come for your job: what GitHub's Copilot 'AI assistant' means for coders

We live in an attention economy that thrives on extracting screen time from users; in an economy where automation drives corporate profit but not necessarily higher wages, and where art is commodified as content; in a social context where it is increasingly hard to distinguish real from fake; in sociotechnical structures that too easily encode biases in the AI models we train. In these circumstances, AI can easily do harm.

How can we steer these new AI technologies in a direction that benefits people? I believe one way to do this is to design AI that collaborates with, rather than replaces, humans.

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AI art is everywhere right now. Even experts don't know what it will mean - The Conversation

Winter may hve arrived early for AI and machine learning startups. After years of rosy projections, growth and investor enthusiasm, a new report from PitchBook shows that VC activity in the AI sector declined precipitously over the past few months.

Deal value growth in AI startups was down 27.8% quarter over quarter in Q2 2022, with overall investments reaching $20.2 billion across 1,340 deals. Year to date, VCs have funneled $48.2 billion into AI startups across over 3,000 deals which sounds healthy but actually represents a 20.9% year-over-year dip.

The IPO wave of 2021 came and went without significant outcomes for horizontal AI startups, leaving questions as to the market size for AI software and opportunities for AI chip companies.PitchBook senior analyst Brendan Burke

AI funding declined across all deal stages, per PitchBook data. Excluding angel and seed rounds, early-stage investments hit $4.2 billion, a drop from $5.6 billion in Q1 and down 35% from the same quarter last year. Meanwhile, later-stage funding declined from $18.3 billion in Q1 to $13.4 billion in Q2 a 48% decrease compared to last year.

As VC deal value and deal count in AI startups reach their lowest levels since Q4 2020, its not just investors that are pulling back.

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Tighter VC capital forces AI startups to face the music - TechCrunch

SAN JOSE, Calif.--(BUSINESS WIRE)--Inspur Systems, a leading data center, cloud computing and AI solutions provider, announced that Inspur AI servers achieved record-breaking results with massive performance gains in the newly-released MLPerf Inference v2.1 AI benchmark results. Inspur AI servers took the lead in more than half of the tasks in the Closed division, posting improvements in performance over 100% in multiple tasks compared to previous results.

Inspur AI Servers were top ranked in 19 out of 30 tasks in the Closed division, which offers an apples-to-apples performance comparison between submitters. Among them, Inspur AI servers won 12 titles out of 16 tasks in the datacenter category and 7 titles out of 14 tasks in edge category. Inspur successfully defended 11 performance records and saw performance improvements of approximately 100% in several tasks like BERT (natural language processing) and 3D U-Net (medical image segmentation).

Strong lead in BERT, greatly improving Transformer performance

21 global companies and research institutions submitted more than 10,000 performance results for the Inference v2.1 benchmarks. The Inspur NF5468M6J AI Server has a pioneering design with 24 GPUs in a single machine. Inspur improved BERT inference performance, which is based on Transformer architecture, with strategies including in-depth optimization of Round Robin Scheduling of GPUs to make full use of the performance of each GPU, enabling the completion 75,000 question-and-answer tasks per second. This is a massive 93.81% jump compared with the previous best performance in the v2.0 results. It is also marked the 4th time that an Inspur AI Server was the benchmark leader for the MLPerf inference BERT task.

The Inspur NF5468M6J AI Server achieved record-breaking performance that was 20% higher than the runner-up In the BERT task. The success of NF5468M6J is due to its excellent system design. It supports up to 24 A100 GPUs with a layered and scalable computing architecture, and earned 8 titles with excellent performance. Among the participating high-end mainstream models utilizing 8 GPUs with NVLink technology, Inspur AI servers achieved top results in 7 out of 16 tasks in the Data Center category, showing leading performance among high-end models. Among them, NF5488A5, Inspurs flagship high-performance AI server, supports 8 third-generation NVlink interconnected A100 GPUs and 2 AMD Milan CPUs and 8 GPUs in a 4U space. The NF5688M6 is an AI server with extreme scalability optimized for large-scale data centers. It supports 8 A100 GPUs and 2 Intel Ice Lake CPUs and 8 GPUs, and supports up to 13 PCIe Gen4 I/O expansion cards.

Optimization on algorithm and architecture, further enhancing performance

Inspur is the first to apply the hyperparameter optimization solution in MLPerf training, which greatly improves performance. Inspur pioneered a ResNet convergence optimization solution. In the ImageNet dataset, only 85% of the original iteration steps were used to achieve the target accuracy. This optimization scheme improved training performance by 15%. Inspur is also the first to use the self-developed convolution merging algorithm plugin operator solution in the MLPerf Inference benchmarks. The algorithm improves performance from 123TOPS to 141TOPS, a performance gain of 14.6%.

In terms of architecture optimization, Inspur took the lead in using the JBOG solution to greatly improve the ability of Inspur AI servers to adopt a large number of GPUs in a single node. In addition, the high-load multi-GPU collaborative task scheduling and the data transmission performance between NUMA nodes and GPUs are deeply optimized. This enables a linear expansion of CPU and GPU utilization and the simultaneous operation of multiple concurrent task, which greatly improves performance.

Inspur is committed to the full stack innovation of an AI computing platform, resource platform and algorithm platform, and jointly accelerates the process of AI industrialization and intelligent development of various industries through its MetaBrain ecosystem partners.

As a member of MLCommons, Inspur has actively promoted the development and innovation of the MLPerf benchmark suite, participating in the benchmarks 10 times and winning multiple performance titles. Inspur continues to innovate in aspects such as overall system optimization, software and hardware synergistic optimization, and reduction of energy consumption ratio, constantly breaking MLPerf performance records, and sharing the technology with the MLCommons community, which has been used by a large number of participating manufacturers and is widely used in subsequent MLPerf benchmarks.

To view the complete results of MLPerf Inference v2.1, please visit:https://mlcommons.org/en/inference-datacenter-21/ https://mlcommons.org/en/inference-edge-21/

About Inspur Systems

Inspur Systems is a leading data center, cloud computing and AI solutions provider, ranked among the worlds top 3 server vendors. It is Inspur Informations San Francisco-based subsidiary company. Inspurs cutting-edge hardware products and designs are widely delivered and deployed in major data centers around the globe, serving important technology arenas like open computing, cloud, AI and deep learning. Inspur works with customers to develop purpose-built, performance-optimized solutions that empower them to tackle different workloads, overcome real-world challenges, and grow their business. To learn more, visit https://www.inspursystems.com.

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Inspur AI Servers Achieve Record-Breaking Results in the Latest MLPerf v2.1 Inference Benchmarks - Business Wire

French biotech company WhiteLab Genomics has raised $10 million in funding for an AI platform designed to aid the discovery and development of genomic therapies.

Founded out of Paris in 2019, recent Y Combinator (YC) graduate WhiteLab Genomics provides gene and cell therapy companies with predictive software simulations to expedite the design of gene and cell therapies. Gene therapy, for the uninitiated, is an emerging treatment that involves replacing missing or defective genes in cells to correct genetic disorders, while cell therapy is about altering a cell or sets of cells to trigger an effect throughout the body.

Thousands of diseases, including cystic fibrosis, Parkinsons and Alzheimers stem from flaws in an individuals DNA, and emerging research in gene and cell therapies may eventually treat such conditions at their source, supplanting the need for drugs or surgery.

However, such therapies are typically costly to develop with no guarantee that theyll work. Current methodologies in developing new gene and cell therapies typically involves a trial-and-error approach, according to WhiteLab Genomics CEO and co-founder David Del Bourgo, whereby scientists make a scientific hypothesis and test it in a lab: if successful, it progresses to the next stage, but if its unsuccessful, they go back to square one with a different hypothesis. And this is where WhiteLab Genomics enters the fray, with a computational approach that meshes machine learning and deep learning techniques to process multiple scientific hypotheses at once, looking at different genetic variants to predict the best molecular design for the therapy based on the objectives.

Gene and cell therapies still suffer from poor efficacy, immunological secondary effects and very high development cost, Del Bourgo explained to TechCrunch. We provide customers with exhaustive predictive models combining genetics and computational biology in order to design and select the top candidates to be tested in the lab.

WhiteLab Genomics founders: CEO David Del Bourgo and Chief Science Officer Julien Cottineau. Image Credits: WhiteLab Genomics

As with just about any AI models, WhiteLab Genomics uses myriad datasets to train its algorithms spanning genomic, RNA, oncologic and protein repositories, in addition to an exhaustive set of documentation such as public scientific and clinical research data.

We also partner with specialized institutions to enrich the models with additional non-public data, Del Bourgo added. We train and validate our algorithms on well-characterized datasets that are interconnected within our Knowledge Graph Database. These included public databases, proprietary data and datasets from our partners. It predicts biological features from new genetic sequences to build new therapeutic vectors and generates new therapeutic constructs.

In terms of the kinds of treatments WhiteLab Genomics is helping to develop, Del Bourgo said that the company is currently working on projects including DNA-based therapies for metabolic conditions such as lysosomal diseases, as well as cell stem therapies for blood diseases such as sickle cell disease and immuno-gene therapies to treat cancers.

Elsewhere, anumber of companies are using AI to aid drug discovery, such as BenevolentAI, which has raised sizeable investments from the VC world, while theres TechCrunchs Startup Battlefield winner Cellino, which recently raised $80 million to develop cell therapies using AI. WhiteLab Genomics is similar, but its working with both gene and cell therapy companies.

Its also worth noting that WhiteLab Genomics represents a growing roster of French startups to successfully infiltrate the YC ranks. Of the 40 European companies in WhiteLab Genomics Winter 22 cohort, five were French, a figure that rose to eight of 34 for the Summer 22 tranche the second highest number from the whole of Europe.

With $10 million in the bank from French investor Omnes Capital and biopharmaceutical heavyweight Debiopharm, WhiteLab Genomics is now well-financed to build out its platform and double-down on its partnerships with the scientific realm, which so far has included notable collaborations with the French National Institute of Health and Medical Research (Inserm) and Genethon Laboratories, among other pharmaceutical and biotech companies.

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How WhiteLab Genomics is using AI to aid gene and cell therapy development - TechCrunch

Its that time of year again. This morning, Y Combinator (YC) hosted a demo day for its 2022 Summer Cohort the 35th demo day in the incubators history. Featuring founders from 30 countries and startups across sectors including developer tools, fintech and healthcare, the day saw no shortage of compelling pitches.

The competition was fiercer than usual, owing to YCs decision in early August to cut the batch size by 40% to around 250 companies in light of economic headwinds. But a particular category of startup stood out: those applying AI and machine learning to solve problems, especially for business-to-business clients.

This year had only 14 such startups compared to 20 last year, which makes sense as the overall cohort is also smaller. But the batches share a unifying theme: sales. Their products largely target hurdles in sales and marketing at a time when businesses are up against recessionary pressures.

Economic challenges aside, the large addressable market makes sales an attractive problem for startups to tackle. Grand View Research pegged the sales force automation software market alone at $7.29 billion in 2019.

Pilot AI is developing a tool for sales reps that automatically translates call recordings into structured data that then directly updates a customer relationship management (CRM) system. The idea is to save reps time, and to assure their managers that the pipeline data is up to date.

Its worth noting that other platforms like Fireflies.ai and Microsofts Viva Sales also do this. But Pilot AI founder Max Lu, previously a software engineer at Salesforce, says his product is more thorough than most, and can generate a summary of each call as well as data points that map to CRM fields and questions asked by reps, in addition to key parts of the recipients answer.

Image Credits: Pilot AI

Typewise is also in the sales space, but it focuses on text prediction across web apps via a browser extension and server-side API. Initially developed as a smartphone app, Typewise which claims to have Fortune 500 customers in the e-commerce and logistics industries can autocomplete sentences, insert smart snippets, automatically reply to messages and check for style and grammar consistency.

It sounds a little like TextExpander and Magical. But founder David Eberle says that Typewise is compatible with any CRM system and can be customized to a companys data, with an analytics component that suggests which words and phrases to use.

YC Summer 2022 AI startups that didnt fall within the sales and marketing tech category tended to focus on dev tools, another lucrative avenue to growth. Considering that 55% of developers struggle to find the time to build internal apps in the first place, according to one recent survey, VCs certainly see an opportunity: they invested $37 billion last year into startups creating dev tools.

Monterey AI tackles a decidedly different part of the product lifecycle: Development. Founder Chun Jiang pitches it as a co-pilot for product development that replaces documents with workflows that automatically generate product specs, including feature ideas, metrics, designs and launch plans.

Using Monterey, customers pick a product template based on their use case (e.g. software as a service) and configure the inputs, checking dependencies to solve conflicts. Jiang says the platform can uncover cross-team conflicts and dependencies while providing a birds-eye view of the portfolio to align features.

Image Credits: Monterey AI

Dev Tools AI could perhaps be used in tandem with Monterey AI.

Dev Tools AI offers a library designed to make it easier to write tests for web apps in existing dev environments by simply drawing a box over a screenshot. Applying computer vision, it finds elements on webpages like search boxes and buttons, and can even see controls within web games. It can also test for crawl errors on pages, including broken links, 404s and console errors.

As founder Chris Navrides points out, writing end-to-end web tests is a traditionally time-consuming process, requiring one to dig around in the page code multiple times as the tested app evolves. Assuming Dev Tools AI works as intended, it could be a valuable addition to quality assurance testing teams arsenals.

Maya Labs is creating a platform for translating natural language into code. Similar to GitHubs Copilot, Maya incrementally generates programs and shows results in response to steps in English.

One of Mayas founders, Sibesh Kar, says that the service builds apps by using a combination of conditional logic, AI-powered search and classification, fine-tuned language models and template generation. Currently, Maya can query and plot data from an external source like Google Sheets, Notion or Airtable, and perform actions on that data, like sending an email, uploading a file or updating a database entry.

The long-term goal is to extend Maya to tasks like web navigation, connecting APIs and workflow automation, which given the current state of AI text-to-language systems seems within the realm of possibility.

For those who prefer a hands-on approach to programming, Hello claims to use AI to instantly answer developers technical questions with explanations and relevant code snippets from the web. The platform is powered by large language models (think GPT-3) that reference several sources to find the most likely answers, according to co-founder Michael Royzen.

When Hello users submit a query, the service pulls and re-ranks raw site data from Bing, and then extracts understanding using the aforementioned models. A different set of models translates the results into human-readable answers.

Image Credits:Hello

Another startup with language models at its core is NuMind, which provides data scientists, data analysts and software engineers a tool for creating custom natural language processing models. Leveraging large language models similar to GPT-3, NuMind can be used to, for example, find which job offers the best match a given resume on a recruitment platform.

NuMind founders Etienne Bernard (the former head of machine learning at Wolfram Research) and Make.org co-founder Samuel Bernard claim that interest in the company is quite high, with its paying customer base growing to nine in the span of a month.

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7 AI startups that stood out in YCs Summer 22 batch - TechCrunch