Anthropic recently made Claude Artifacts available to all users on iOS and Android, allowing anyone to easily create apps without writing a single line of code. AIM tried its hand at it and successfully created a Cricket Quiz game, Temple Run, and Flappy Bird, all with a single line of prompt in English. 

Debarghya (Deedy) Das, principal at Menlo Ventures, used Artifacts to build a Splitwise-like app. “With Claude launching on iOS today, I can now generate the Splitwise app instead of paying for Pro,” he said. 

“Claude Artifacts allows you to go from English to an entire app and share it!” he added, saying that his friend, a product manager who couldn’t code, now creates apps in minutes. “The cost of a lot of software is nearing ~$0.”

This brings us to question if this could be the end of App Stores. Groq’s Sunny Madra thinks this is the beginning of “The Build Your Own (BYO) era. Since Artifacts are shareable, anyone can use the apps you build, and they can be shared on any social media platform as a link.

Several users experimented with Claude Artifacts by building different apps. 

“Claude 3.5’s artifacts, now shareable, can help teach. In class, startup financing can be hard to explain. Now I just asked, “Create an interactive simulation that visually explains payoff differences for a startup and VC with liquidation preference…” Ethan Mollick, associate professor at the Wharton School of the University of Pennsylvania, wrote on X. 

Similarly, Allie K Miller, AI advisor and angel investor, used it to build a calendar and an AI quiz, which took less than two minutes! 

The best part about Artifacts is that it is mobile-friendly and responsive. “Using Claude 3.5 Sonnet, you can generate artifacts (e.g., code snippets, text documents, or website designs) and iterate on them right within the same window,” exclaimed Elvis Saravia, the co-founder of DAIR.AI. 

On-Demand Software

When using mobile phones, we often search for apps that can solve our specific needs. For example, if you’re into fitness, you might download an app that offers various workouts. However, the app may not provide the customisation you seek. Now, instead of relying on downloads, you can create your own personalised apps that cater specifically to your needs. 

“On demand software is here,” said Joshua Kelly, chief technology officer, Flexpa, a healthcare tool company. Using Artifacts, he built a simple stretching time app for his runs in just 60 seconds.

Other than just giving prompts, users can now also share previously made websites or apps, and Claude can generate an exact replica. 

“You can now take a photo of something you want to replicate, give it to AI, and it outputs the code with a preview right on your iPhone,” posted Linas Beliūnas, director of revenue at Zero Hash, on LinkedIn.

On the internet, one can find several apps built using Claude Artifacts, such as the Rubik’s Cube Simulator, Self-Playing Snake Game, Reddit Thread Analyzer, Drum Pad, and Daily Calorie Expenditure.

Apart from building apps, Artifacts has the potential to greatly impact education. “Any piece of content— whether it’s a screenshot, PDF, presentation, or something else—can now be turned into an interactive learning game,” said AI influencer Rowan Cheung.

The End of No-Code Platforms?

Claude Artifacts is going to be a big threat to no-code and low-code app builder platforms such as AppMySite, Builder.ai, Flutter, and React Native. 

“Claude Artifacts are insane — I cannot believe how good the product is. You can ask it to build most little internal tools in minutes (at least, the UI) and customize further via code. Feels like a superpower for semi-technical people,” posted a user on X. 

Moreover, Claude, when put together with Cursor AI, has simplified the process of making apps. “So I’m building this box office app in React Native and I thought I’d try Cursor with Claude 3.5 and see how far I’d get. The backend is django/psql that’s already in place,” said another user on X. “Starting from scratch, I have authenticated with my server to log in users, issue tickets, register tickets, scan ticket QR codes, and send email/sms confirmations,” he added. 

Claude is set to rapidly democratise app development, potentially eliminating the need for an App Store. It will enable anyone to build apps based on their specific needs, complete with personalised UI and UX.

Moreover, building an app for the iOS App Store is challenging. Apple charges a standard 30% commission on app sales and in-app purchases, including both paid app downloads and digital goods sold within the apps. 

The company enforces rigorous guidelines that apps must adhere to, covering aspects such as user interface design, functionality, and privacy. Many apps are rejected for minor violations, and these guidelines are frequently updated, requiring developers to stay informed and adapt quickly.

However, for now, Claude allows anyone to build anything without any charges and lets users experiment to see if something is working or not. Even if someone wants to publish an app built using Claude on the iOS App Store, that is definitely an option.

Interestingly, Apple recently announced that, for the first time, it will allow third-party app stores on iOS devices in the EU. This change enables users to download apps from sources other than Apple’s official App Store, providing more options for app distribution and potentially reducing costs for developers.

Better than ChatGPT 

OpenAI previously introduced ChatGPT plugins, enabling users to create custom GPTs for their specific tasks. However, these plugins do not compare to Artifacts, which allows users to visualise their creations. 

While the Plugins Store was billed as an ‘iOS App Store moment’, it failed to meet the expectations and ended up being a hot mess. 

Moreover, during DevDay 2023, OpenAI chief Sam Altman launched a revenue-sharing programme which was introduced to compensate the creators of custom GPTs based on user engagement with their models. 

However, many details about the revenue-sharing mechanism remain unclear, including the specific criteria for payments and how the engagement would be measured.

“It was supposed to be announced sometime in Q1 2024, but now it’s the end of March, and there are still few details about it,” posted a user on the OpenAI Developer Forum in March. There have been no updates on the matter from OpenAI since then.

The post Anthropic Claude Artifacts to Kill App Store Soon  appeared first on AIM.

The barrier for entry to become a developer is dropping everyday. The most recent phenomenon that everyone is still talking about is Anysphere’s Cursor AI coding tool, which has basically made everyone a developer. Now, there are more tools coming up in the same category such as Codeium, Magic, and Zed AI, all of them trying to come up with the same formulae. 

This definitely brings in the question – what would happen to the software developers of today? Graduating out of colleges with computer science degrees in a world to compete with the people who are becoming software engineers with AI tools, the turmoil of an average software engineer is real.

The solution is easier said than done – upskill yourselves and focus on higher order things such as building foundational AI. Even 8-year-olds are building apps using Cursor AI in 45 minutes. 

A Profession Like Never Before

Since there is no barrier to entry, no degree requirements, and no regulations about who can join the market, software engineering has become a profession that has never happened ever in the history. There are plenty of opportunities for developers to upskill.

But “upskill to what?” is what people ask. 

The conversation from LLMs to SLMs to coding assistants to AI agents keeps changing so swiftly, it can be challenging to determine which new skills are worth acquiring. This question reflects a broader uncertainty about how to prioritise learning in a field where the next big thing seems just around the corner.

Saket Agrawal, a developer from IIT Guwahati, said that it is not as much about the technological shift but the advancement of automation tools that reduce the time and efforts for the same skills. “I don’t see any big threat to existing software skills suddenly and software has been the field all the time which needs continuous skills updation based on requirement without leaving your old skills instantly,” he said. 

Another user on X put it in a funny way. “Software engineers need more updates than my grandma’s Windows 95. Ever tried explaining AI to her? It’s like defining gluten-free bread to a caveman!”

It is widely discussed that a lot of software engineering jobs are dying. “Winter is coming for software engineering,” said Debarghya Das from Menlo Ventures, saying that many of the current software engineering jobs would become a distant memory. 

Scott Stouffer adds another layer to this conversation by suggesting that some are experiencing an upgrade in their lives at a pace that surpasses others. This notion of being “upgraded” faster could imply a divide between those who adapt quickly to technological advancements and those who struggle to keep up.

LLMs to Upskill?

While there is a very interesting caveat to all of this conversation around upskilling. Hardcore skilled developers believe that leveraging tools such as Cursor and others can take them to another level where the new developers would never be able to reach. Yann LeCun has already told developers getting into the AI field to not work on LLMs.

Andrej Karpathy recently said that the future of coding is ‘tab tab tab’ referring to auto code completion tools such as Cursor. Further in the thread, he added that with the capabilities of LLM shifting so rapidly, it is important for developers to continually adapt the current capabilities.

Some people are sceptical if they even should get into the computer science field anymore. “…if I was new to programming I would be too tempted to skip actual learning in favour of more LLM usage, resulting in many knowledge gaps,” said a user replying to Karpathy. This truly feels like the true way forward for many developers. 

This is similar to what Francois Chollet, the creator of Keras, said a few months ago. “There will be more software engineers (the kind that write code, e.g. Python, C or JavaScript code) in five years than there are today.” He added that the estimated number of professional software engineers today is 26 million, which would jump to 30-35 million in five years.

This is because developers who are proficient with coding without code generators can never be replaced. People who built programming languages and foundational tools are still very well versed with coding than people who are just using Cursor to build apps. Sure, there can be an abundance of people building apps in the future, but the scope would just be limited to that.

Meanwhile, highly skilled 10x developers would be focusing on leveraging such tools, or possibly finding flaws in them, to create even better software. So to say, creating the next Cursor or ChatGPT.

There is an abundance of things that can be done. For instance, focusing on enhancing hardware or building infrastructure for running future workloads can only be comprehended by experts in the field. For example, companies such as Pipeshift AI, Groq, Jarvis Labs, and many others who are working on different problems than coding. 

The truth is that such AI tools can never replace human intelligence or jobs, only augment them. “Generating working code is only a part of the responsibility,” said VJ’s Insights on a post on X. Though “Yes, if you are someone who *just* writes code, you need to start thinking differently.”

In the near future, there are predictions that the future of software engineering would be about managing a team of AI agent engineers, and telling them how to code. This will make every engineer akin to an engineering manager, delegating basic tasks to coding agents while focusing on higher-level aspects such as understanding requirements, architecting systems, and deciding what to build.

It is high time that software engineers start upskilling themselves, and currently, it looks like using generative AI tools is the best way forward, not without them. Who knows, you might also become a solo-entrepreneur building a billion dollar company alone. 

The post Software Engineers Have to Upskill Faster Than Anyone Else appeared first on AIM.

What does it take to qualify as an AI startup? At what stage do they need financial support? And lastly, what should the ideal mode of financing be? These critical topics came up for discussion when government officials recently met with key industry figures.

The focus of the meeting was AI startups in the context of the IndiaAI Mission. 

Notable attendees of the meeting included Google, NVIDIA, and Microsoft, and representatives from AI startups such as Jivi and DronaMaps were also present, the Hindustan Times said.

It’s encouraging to see the government recognise the rapid growth of AI startups across India and acknowledge the significant role they could play in driving the country’s economy in the coming years.

On a recent podcast, Rahul Agarwalla, managing partner at SenseAI Ventures, said he witnessed about 500 new AI startups emerge in the past six months, which is a massive number.

Based on the rate at which AI startups are popping up in the country, Agarwalla believes India could soon have 100 AI unicorns. While it remains to be seen when that happens, the budding AI ecosystem in India will indeed need support from the government beyond regulatory favours.

What Qualifies as an AI Startup?

A key topic discussed at the meeting was the criteria to define an AI startup. 

Attendees highlighted to the government that simply having ‘AI’ in their name does not automatically make a startup an AI-focused company.

In response, stakeholders proposed a rating system, which builds credibility among startups and would in turn make them eligible for government funding. Not everyone will make the cut though. 

Unfortunately, in the startup world, a majority of them do not live long enough to see the light at the end of the tunnel. 

Stakeholders recommend that rather than spreading a small amount of funding thin across many startups, the government should focus on identifying those with significant potential and provide them with targeted financial support.

Earlier, the government had allocated INR 10,372 crore as part of the India AI Mission – a part of which will be used to fund startups.

Should the Government Play the VC?

According to a Tracxn report, Indian AI startups raised $8.2 million in the April-June quarter, while their US counterparts raised $27 billion during the same period.

While not many Indian startups are building LLMs, which cost billions of dollars, the funding for AI startups in India still remains relatively low.

The government, under the IndiaAI Mission, is weighing options to fund AI startups and deciding how best to do so. One bold proposal on the table was taking equity stakes in these emerging companies.

The government had previously suggested taking the equity route as part of the second phase of the designed-linked incentive (DLI) scheme for semiconductor companies. However, the thought was not well received by many in the industry. 

“[I] don’t understand the logic of the government trying to become a venture capital firm for chip design companies. This move is likely to be ineffective and inefficient,” Pranay Kotasthane, a public policy researcher, said back then.

They fear government taking equity could lead to government influence over company operations, and historically, public sector companies in India have often underperformed. Moreover, it could push other venture capitalists away.

Access to Datasets and Compute 

Stakeholders were also quick to point out that more than financing, what the startups need is help in terms of compute. 

According to Abhishek Singh, additional secretary, ministry of electronics and information technology (MeitY), the government plans to disburse INR 5,000 crore of the allocated INR 10,372 crore to procure GPUs.

The government was quick to identify the need for compute, especially for Indian startups, researchers, and other institutions. In fact, last year, the government revealed its intention to build a 25,000 GPU cluster for Indian startups. 

Interestingly, PM Narendra Modi also met Jensen Huang, the CEO of NVIDIA, the company producing the most sought-after GPUs in the market, during his visit to India in 2023.

(Source: NVIDIA)

The Indian Express reported earlier this month that the government had finalised a tender to acquire 1,000 GPUs as part of the IndiaAI Mission. These GPUs will provide computing capacity to Indian startups, researchers, public sector agencies, and other government-approved entities.

Besides access to compute, the stakeholders also urged the government to make the datasets under the IndiaAI Mission available as soon as possible. The datasets will grant startups access to non-personal domain-specific data from government ministries to train models.

Notably, the Bhashini initiative is playing a crucial role in democratising access to Indic language datasets and tools for the Indian ecosystem.

India Startup 2.0 

While the government’s recognition of the funding gap in AI startups and its willingness to provide financial support is encouraging, it is equally important that the government creates a favourable environment for these businesses to thrive.

In line with this, the government launched the Startup India programme in 2016 to foster a robust ecosystem for innovation and entrepreneurship in the country. 

This initiative was designed to drive economic growth and create large-scale employment opportunities by supporting startups through various means. Perhaps, the need of the hour is a similar programme designed specifically for AI startups.

As part of the startup programme, the government identified 92,000 startups and, in addition to funding, provided support such as income tax exemption for three years, credit guarantee schemes, ease of procurement, support for intellectual property protection, and international market access.

Moreover, over 50 regulatory reforms were undertaken by the government since 2016 to enhance the ease of doing business, ease of raising capital, and reduce the compliance burden for the startup ecosystem.

Now, a similar ecosystem needs to emerge for AI startups as well, which fosters innovation, provides essential resources, and facilitates collaboration among researchers, developers, and investors to drive growth and success in the field.

The post India’s AI Startup Boom: Govt Eyes Equity Stakes and GPU Support appeared first on AIM.

There are millions of small businesses in the world, and in the future, all of them could have AI agents carry out functions like customer support and sales. Meta CEO Mark Zuckerberg recently said there could be more AI agents in the world than humans.

Venture capitalist Vinod Khosla predicts that most consumer interactions online will involve AI agents handling tasks and filtering out marketers and bots. However, this raises a fundamental question: How do you differentiate between an AI agent and a human? Or worse – what if bad actors start using these agents?

In this context, Ben Colman, the co-founder and CEO of Reality Defender, made a bold statement: “Even someone with a PhD in computer science and computer vision can’t distinguish between what’s real and what’s fake.”  

Malicious actors have exploited fake identities to commit fraud, spread disinformation, and pull off countless deceptive schemes. With AI agents, these bad actors will be able to scale their operations with unprecedented efficiency and finesse.

What’s the Solution?

A recent research paper ‘Personhood Credentials’ (PHCs) explores an approach to tackle the growing challenge of distinguishing real people from AI online. These digital credentials allow users to prove they are human without revealing any personal information. 

The study is authored by researchers, including Srikanth Nadhamuni, former head of technology, Aadhaar; Steven Adler from OpenAI; Zoë Hitzig from the Harvard Society of Fellows; and Shrey Jain from Microsoft – all of whom have significant expertise in AI and technology.

The paper outlines two types of PHC systems – local and global – highlighting that they don’t need to rely on biometric data.

Imagine a government or another digital service provider issuing a unique credential to each person. To prove their ‘humanity’, users would employ clever cryptographic techniques called zero-knowledge proofs, allowing them to confirm their identity without revealing the specifics. 

These credentials would be stored securely on personal devices, offering a layer of online anonymity. 

The researchers suggest that PHCs could either replace or work alongside existing verification methods like CAPTCHAs and fingerprint scans, which are increasingly struggling to keep up with AI. 

Another possible solution could be a simple declaration – letting people know that they are interacting with an AI. Praveer Kochhar, the co-founder of KOGO Tech Labs, emphasised the importance of transparency. “Immediately as a call starts, there has to be a declaration that you’re talking to an AI agent. Once you declare that, it’s very straightforward,” Kochhar told AIM.

Earlier in April, a user on X, Alex Cohen, shared that Bland AI, a San Francisco-based firm, uses a tool for sales and customer support that can be programmed to make callers believe they are speaking with a real person.

As seen in the post above, had the bot not acknowledged being an “AI agent”, it would have been nearly impossible to differentiate its voice from that of a real woman.

Well, now chatbots are being deployed to fight against phone scammers. 

Dali Kaafar, a researcher at Macquarie University, Australia, and his team, have designed AI chatbots Malcolm and Ibrahim, to address the concern.

So, when scammers call, they think they’ve reached a potential victim. But they talk to “Malcolm”, an elderly man with an English accent, or “Ibrahim”, a polite man with an Egyptian accent. 

However, the same innovation can again be used to scam innocent people. So, in scenarios like these, mere declarations or a centralised credential system may not help you identify an AI agent. 

Are they Good Enough?

A McAfee Corp survey revealed that 69% of Indians think they don’t know or cannot tell the difference between an AI voice and a real voice. Also, there were reports earlier regarding the rise of fraudulent robocalls amid US elections.

So, how apt are these solutions for the rising AI crimes? 

Johanne Ulloa, the director of solutions consulting at LexisNexis Risk Solutions, warns, “Chatbots can still be misused to enhance the effectiveness of phishing emails, as there’s nothing to prevent them from generating texts that prompt customers to log in to an online account under the guise of security.” 

With the advent of text-to-speech technology, AI’s potential to compromise security grows even more concerning. Earlier this year, OpenAI showed off the voice capabilities of their GPT-40 model, which eerily sounded like Scarlett Johansson.

“These AI systems can take any text and replicate a sampled voice, leading to situations where people have received messages from what appeared to be relatives, only to discover their voices had been spoofed,” Ulloa noted. 

Many startups today are building AI agents that can be integrated via telephony, WhatsApp, and mobile applications. As AI agents become more prevalent, there is a need for more discussions in the industry and research for better tools to mitigate the risks of technology misuse.

The post How Do You Differentiate an AI Agent from a Human? appeared first on AIM.

NVIDIA may reign as the king of GPUs, but competition is heating up. In recent years, a wave of startups has emerged, taking on the Jensen Huang-led giant at its own game.

Tenstorrent, a startup led by Jim Keller, the lead architect of AMD K8 microarchitecture, is developing AI chips that the company claims perform better than NVIDIA’s GPUs.

“We have a very power-efficient compute, where we can put 32 engines in a box, the same size as NVIDIA puts eight. With our higher compute density and similar power envelope, we outperform NVIDIA by multiples in terms of performance, output per watt, and output per dollar,” Keith Witek, chief operating officer at Tenstorrent, told AIM.

(Wormhole by Tenstorrent)

NVIDIA’s chips used in the data centres need silicon interposers like HBM memory chips. Companies like Samsung and SK Hynix, along with NVIDIA, have also made millions selling these chips. However, Tenstorrent chips eliminate the need for these chips.

Similarly, Cerebras Systems, founded by Andrew Feldman in 2015, has developed chips to run generative AI workloads such as training models and inference. Their chip, WSE-3– is the world’s largest AI chip– with over 4 trillion transistors and 46225mm2 of silicon.

Check: Difference Between NVIDIA GPUs – H100 Vs A100

The startup claims its chips are 8x faster than NVIDIA DGX H100 and are designed specifically to train large models.

(World’s largest AI chip- WSE-3)

Startups Building for the Inference Market

There are startups developing chips designed specifically for inferencing. While NVIDIA’s GPUs are in great demand because they are instrumental in training AI models, for inference, they might not be the best tool available. 

D-Matrix, a startup founded by Sid Sheth, is developing silicon which works best at inferencing tasks. Its flagship product Corsair is specifically designed for inferencing generative AI models (100 billion parameter or less) and is much more cost-effective, compared to GPUs. 

“We believe that a majority of enterprises and individuals interested in inference will prefer to work with models up to 100 billion parameters. Deploying larger models becomes prohibitively expensive, making it less practical for most applications,” he told AIM.

Another startup that is locking horns with NVIDIA in this space is Groq, founded by Jonathan Ross in 2016. According to Ross, his product is 10 times faster, 10 times cheaper, and consumes 10 times less power.

Groq is designed to provide high performance for inference tasks, which are critical for deploying AI models in production environments.

Recently, another player, Cerebras, announced its Cerebras inference, which they claim is the fastest AI inference solution in the world. It delivers 1,800 tokens/sec for Llama3.1 8B and 450 tokens/sec for Llama3.1 70B, which is 20x faster than NVIDIA GPU-based hyperscale clouds.

Challengers in the Edge AI Market

While NVIDIA may have made its name and money by selling GPUs, over the years, it has also expanded in other segments, such as developing chips for humanoids, drones, and IoT devices.

SiMa.ai, a US-based startup with strong roots in India, is building chips which can run generative AI models on the embedded edge. Founded by Krishna Rangasayee in 2018, the startup takes NVIDIA as its biggest competitor.

Rangasayee believes multimodal AI is the future and the startup’s second-gen chip is designed to run generative AI models on the edge– on cars, robotic arms, humanoids, as well as drones.

“Multimodal is going to be everywhere, from every device to appliances, be it a robot or an AI PC. You will be able to converse, watch videos, parse inputs, just like you talk to a human being,” he told AIM.

Notably, SiMa.ai’s first chip, designed to run computer vision models on edge, beat NVIDIA on the ML Perf benchmarks. Another competitor of NVIDIA in this space is Hailo AI. It is building chips that run generative AI models on the edge.

Everyone Wants a Piece of the Pie 

Notably, these startups are not seeking a niche within the semiconductor ecosystem. Instead, they are focused on delivering top-tier products and are unafraid to compete directly with NVIDIA.

They all want a piece of the pie and are already locking horns with NVIDIA. 

D-Matrix, for instance, counts Microsoft, which is one of the AI model builders, as its customers. Sheth revealed that the company has customers in North America, Asia, and the Middle East and has signed a multi-million dollar contract with one of its customers. The point here is that Microsoft is one of NVIDIA’s biggest enterprise customers.

Cerebras also counts some of the top research and supercomputing labs as its customers. Riding on the success, the startup plans to go public this year.

Rangasayee previously told AIM that his startup is in talks with many robotics companies, startups building humanoids, public sector companies as well as some of the top automobile companies in the world.

They All Might Lose to CUDA

All these startups have made substantial progress and some are preparing to launch their products in the near future. While having advanced hardware is crucial, the real challenge for these companies will be competing against a monster – CUDA.

These startups, which position themselves as software companies which build their own hardware, have come up with their own software to make their hardware compatible with their customer’s applications. 

For example, Tenstorrent’s open-source software stack Metalium is similar to CUDA but less cumbersome and more user-friendly. On Metalium, users can write algorithms and programme models directly to the hardware, bypassing layers of abstraction.

Interestingly, they have another one called BUDA, which represents the envisioned future utopia, according to Witek. 

“Eventually, as compilers become more sophisticated and AI hardware stabilises, reaching a point where they can compile code with 90% efficiency, the need for hand-packing code in the AI domain diminishes.”

Nonetheless, it remains to be seen how these startups compete with CUDA. Intel and AMD have been trying for years, yet CUDA remains NVIDIA’s moat. 

“All the maths libraries… and everything is encrypted. In fact, NVIDIA is moving its platform more and more proprietary every quarter. It’s not letting AMD and Intel look at that platform and copy it,” Witek said.

The post The Rise of Non-NVIDIA GPUs appeared first on AIM.

It’s now widely recognised that selling AI models is a zero-margin game. The next wave of AI startups must capitalise on LLMs in the application layer to tackle real-world challenges.

“The next billion dollar startups in AI will play on the application layer and not the infrastructure layer,” said AIM Media House chief Bhasker Gupta in a LinkedIn post. 

Gupta added that there is a plethora of problems to be solved using AI, and these startups will localise their solutions while maintaining a broad-based approach.

Echoing a similar market sentiment was Nayan Goswami, the founder and CEO of Chop. “The next major wave of AI innovation will focus on the application layer, where startups will build specialised vertical AI software-as-a-service (SaaS) companies for global markets,” he said. 

Goswami further elaborated that with robust foundational models like Anthropic, Cohere, and OpenAI, along with infrastructure companies like LangChain and Hugging Face advancing rapidly, we’re poised to witness a surge in application-layer startups targeting specific verticals. 

“Think of foundational models as the roadways, and application layers as the vehicles driving on them,” he explained. 

Finding the Right Application to Build is Key 

Andrew Ng, the founder of DeepLearning.AI believes AI’s usefulness in a wide variety of applications creates many opportunities for entrepreneurship. However, he advised budding entrepreneurs to be extremely specific about their ideas for integrating AI. 

For instance, he explained that building AI for livestock is vague, but if you propose using facial recognition to identify individual cows and monitor their movement on a farm, it’s specific enough.  

A skilled engineer can then quickly decide on the right tools, such as which algorithm to use first or what camera resolution to pick.

In a recent interview, Ng explained that the cost of developing a foundation model could be $100 million or more. However, the applications layer, which receives less media coverage, is likely to be even more valuable in terms of revenue generation than the foundation layer.

He also said that unlike foundation models, the ROI on the application layer is higher. “For the application layer, it’s very clear. I think it’s totally worth it, partly because it’s so capital efficient—it doesn’t cost much to build valuable applications. And I’m seeing revenues pick up. So at the application layer, I’m not worried,” he said.

Perplexity AI serves as a strong example by integrating search with LLMs. Rather than building its own foundational models, the startup leverages state-of-the-art models from across the industry, focusing on delivering optimal performance. The company is planning to run ads as well from the next quarter onwards. 

However, not everyone is going to make the cut; some startups are going to fail. Statistically speaking, around 90% of startups don’t survive long enough to see the light at the end of the tunnel.

Ashish Kacholia, the founder and managing director of Lucky Investment Managers, said, “AI is the future but key is how the applications shape up to capitalise on the technology.”

India is the Use Case Capital of AI 

“India is going to be a use case capital of AI. We’ll be very big users of AI, and we believe that AI can significantly help in the expansion of the ONDC Network,” said Manoj Gupta, the founder of Plotch.ai, in an exclusive interview with AIM. 

Similar thoughts were shared by Nandan Nilekani when he said that India is not in the arms race to build LLMs, and should instead focus on building use cases of AI to reach every citizen. He added that “Adbhut” India will be the AI use case capital of the world. 

“The Indian path in AI is different. We are not in the arms race to build the next LLM, let people with capital, let people who want to pedal chips do all that stuff… We are here to make a difference and our aim is to put this technology in the hands of people,” said Nilekani.

Krutrim founder Bhavish Aggarwal believes that India can build its own AI applications. Agreeing with him, former Tech Mahindra chief CP Gurnani said, “It’s time to stop ‘adopting’ and ‘adapting’ to AI applications created for the Western world.”

Gurnani said that the time is ripe for us to build AI models and apps based on Indian data, for Indian use cases, and store them on India-built hardware, software and cloud systems. “That will make us true leaders in the business of tech,” he added. 

Notably, Gurnani recently launched his own AI startup AIonOS. 

Startups Offering More Than LLMs

Lately, several AI startups in India have been building services using generative AI. For example, Unscript, a Bengaluru-based AI startup, is helping enterprises create videos with generative AI. Another video generation startup, InVideo, is estimated to generate $30 million in revenue in 2024. 

Recently, Sarvam AI launched Sarvam Agents. While the startup, backed by Lightspeed, Peak XV, and Khosla Ventures, is not the only company building AI agents, it stands out for its pricing. The cost of these agents starts at just one rupee per minute. 

According to co-founder Vivek Raghavan, enterprises can integrate these agents into their workflow without much hassle.

These agents can be integrated into contact centres and various applications across multiple industries, including insurance, food and grocery delivery, e-commerce, ride-hailing services, and even banking and payment apps.

Similarly, Krutrim AI is making AI shopping co-pilot for ONDC. Khosla Ventures-backed upliance.ai is building kitchen appliances  integrating generative AI. 

Meanwhile, Ema, an enterprise AI company founded by Meesho board member Surojit Chatterjee, recently raised an additional $36 million in Series A funding. 

The company is building a universal AI agent adaptable to a wide array of industries, including healthcare, retail, travel, hospitality, finance, manufacturing, e-commerce, and technology. 

Enterprises use Ema for customer support, legal, sales, compliance, HR, and IT functions. 

Lately, we have observed that Y Combinator is bullish on Indian AI startups, many of which are focused on building AI applications.

For example, the creator of AI software engineer Devika, Mufeed VH, who founded Stition.AI, is now part of YC S24 batch. His startup works around AI cybersecurity for fixing security vulnerabilities in codebases, and is now renamed to Asterisk. 

On the agentic front, Indian co-founders Sudipta Biswas and Sarthak Shrivastava are building AI employees through their startup FloWorks.

Examples are aplenty, with India poised to boast 100 AI unicorns in the next decade. 

In a conversation with AIM, Prayank Swaroop, partner at Accel India, said that the 27 AI startups his firm has invested in over the past few years are expected to be worth at least ‘five to ten billion dollars’ in the future, including those focused on wrapper-based technologies.

There are a host of categories, such as education, healthcare, manufacturing, entertainment, and finance, to explore with generative AI, and this is just the beginning.

The post The Secret to Creating the Next Billion-Dollar AI Startup appeared first on AIM.

Claude + Cursor = AGI. This is basically what the conversation is around coding right now everywhere on X. The hype around Cursor AI was just not enough, and then Andrej Karpathy added that he is choosing to use it instead of GitHub Copilot from now on. Is the future of coding in natural language already here?

Karpathy is the one who said more than a year back that English is the hottest programming language. Now for Cursor, he says, the future is ‘tab tab tab’ and that “The tool is now a complex living thing.” His support for Cursor even made people question if he is working with, or supporting, the team at Anysphere, the creators of Cursor, in some way.

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Further in the thread, he added that with the capabilities of LLM shifting so rapidly, it is important for developers to continually adapt the current capabilities. This argument has resonated by many developers that it is increasingly becoming difficult to catch up with the new tools in coding.

Is it now time to bring back the old days of learning how to code? A user replied to Karpathy saying that they are very grateful that they had time to learn computer science prior to the advent of AI tools. “…if I was new to programming I would be too tempted to skip actual learning in favour of more LLM usage, resulting in many knowledge gaps,” the user added.

Karpathy agreed that it is a very valid concern and he feels that “it’s slightly too convenient to just have it do things and move on when it seems to work.” This has also led to the introduction of a few bugs when he is coding too fast and tapping through big chunks of code.

But, Cursor AI is Here to Stay

Overall, all of the coding assistant tools have given productivity gains for organisations. Before Cursor, developers in companies were relying on GitHub Copilot for coding faster, which was overall reported to have increased the productivity of the teams. But it definitely brings the question of how people would learn coding from scratch from now on.

It is almost as if Cursor is bringing a ChatGPT moment for coding. Earlier, Ricky Robinett, VP of developer relations at Cloudflare, posted a video of his eight-year-old daughter building a chatbot on the Cloudflare Developer Platform in just 45 minutes using Cursor AI, documenting the whole process, even the spelling mistakes while giving prompts! Even Jeff Dean, chief scientist at Google DeepMind was fascinated by it.

“It is the future,” said several developers who reposted the video. People who started using Cursor have started implementing their own tricks. The most common one currently is using Claude 3.5 Sonnet with Cursor AI, as it allows people to use other open source LLMs and architectures such as Replit, Tailwind, React, Vercel, Firebase and many more, as well in their workflows.

Developers have built complex projects using Cursor in just hours, without even writing a single line of code. Plus, using LLMs, the code generator could also explain the use cases and meaning of the code, which in the end assists in learning how to code as well. “If you’re a technical founder, Cursor + Claude 3.5 Sonnet is a legit team of senior software engineers,” said Sahil Lavingia, founder of Gumroad.

Wake Up Call for Others?

Francois Chollet, the deep learning guru and creator of Keras, said that he is interested in watching developers stream while programming using Cursor and Claude and another developer who is really good at coding, and compare how generative AI has worked for the former one. 

Chollet had earlier also said that it would be great if someone could fully automate software engineering as he could move on to working on higher things, which is what Cursor AI is slowly ushering in. 

Meanwhile, there is another tool in the market called Zed, which is released in partnership with Anthropic, the creator of Claude, which several developers claim is better than Cursor and VS Code as well. 

The same was the case with GitHub Copilot and even Cognition Labs’ Devin. Cursor’s capabilities should be a wake up call for Microsoft to make VS Code integration with GitHub Copilot a lot easier. Basically, Cursor is also a glorified VS Code extension. 

Devin, on the other hand, is still to be released, which might create a new era of programming as well. Probably, replacing Cursor AI, or an entire software engineering team. 

It is clear that most of the upcoming generation of developers would want their code to be generated completely by AI, which GitHub Copilot in many instances was failing to do. But the issues with generated buggy code still exist with Cursor, which might get fixed with future iterations. 

The post The Future of Coding is ‘Tab Tab Tab’ appeared first on AIM.

Historically, running AI/ML workloads on Kubernetes has been challenging due to the substantial CPU/GPU resources these workloads typically demand. 

However, things are now changing. The Cloud Native Computing Foundation (CNCF), a nonprofit organisation that promotes the development and adoption of Kubernetes, recently released a new update, Kubernetes 1.31 (Elli). 

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Elli introduces enhancements designed to improve resource management and efficiency, making it easier to handle the intensive requirements of AI and ML applications on Kubernetes.

Enterprises are increasingly turning to cloud native applications, especially Kubernetes, to manage their AI workload. According to a recent Pure Storage survey of companies with 500 employees and more, 54% said they were already running AI/ML workloads on Kubernetes.

Around 72% said they run databases on Kubernetes and 67% ran analytics. Interestingly, the numbers are expected to rise as more and more enterprises turn to Kubernetes. This is because the development of AI and ML models is inherently iterative and experimental. 

“Data scientists continually tweak and refine models based on the evolving training data and changing parameters. This frequent modification makes container environments particularly well-suited for handling the dynamic nature of these models,” Murli Thirumale, GM (cloud-native business unit), Portworx at Pure Storage, told AIM.

Kubernetes in the Generative AI Era

Kubernetes marked its 10th anniversary in June this year. What started with Google’s internal container management system Borg, has now become the industry standard for container orchestration, adopted by enterprises of all sizes. 

The containerised approach provides the flexibility and scalability needed to manage AI workloads.

“The concept behind a container is to encapsulate an application in its own isolated environment, allowing for rapid changes and ensuring consistent execution. As long as it operates within a Linux environment, the container guarantees that the application will run reliably,” Thirumale said.

(Source: The Voice of Kubernetes Expert Report 2024)

Another reason AI/ML models rely on containers and Kubernetes is the variability in data volume and user load. During training, there is often a large amount of data, while during inferencing, the data volume can be much smaller. 

“Kubernetes addresses these issues by offering elasticity, allowing it to dynamically adjust resources based on demand. This flexibility is inherent to Kubernetes, which manages a scalable and self-service infrastructure, making it well-suited for the fluctuating needs of AI and ML applications,” Thirumale said.

NVIDIA, which became the world’s most valuable company for a brief period, recently acquired Run.ai, a Kubernetes-based workload management and orchestration software provider. 

As NVIDIA’s AI deployments become more complex, with workloads distributed across cloud, edge, and on-premises data centres, effective management and orchestration get increasingly crucial. 

NVIDIA’s acquisition also signifies the growing use of Kubernetes, highlighting the need for robust orchestration tools to handle the complexities of distributed AI environments across various infrastructure setups.

Databases Can Run on Kubernetes

Meanwhile, databases are also poised to play an important role as enterprises look to scale AI. Industry experts AIM has spoken to have highlighted that databases will be central in building generative AI agents or other generative AI use cases.

As of now, only a handful of companies are training AI models. Most of the remaining enterprises in the world will be finetuning their own models and will look to scale with their AI solutions very soon. Hence, databases that can scale and provide real-time performance will play a crucial role.

“AI/ML heavily rely on databases, and currently, 54% of these systems are run on Kubernetes—a figure expected to grow. Most mission-critical applications involve data, such as CRM systems where data is read but not frequently changed, versus dynamic applications, like ATMs that require real-time data updates. 

“Since AI, ML, and analytics are data-intensive, Kubernetes is becoming increasingly integral in managing these applications effectively,” Thirumale said.

Replacement for VMware

Broadcom’s acquisition of VMware last year also impacted the growing usage of Kubernetes. The acquisition has left customers worried about the pricing and integration with Broadcom.

“It’s a bundle, so you’re forced to buy stuff you may not intend to,” Thirumale said. 

Referring to the survey again, he pointed out that as a result around 58% of organisations which participated in the survey plan to migrate some of their VM workloads to Kubernetes. And around 65% of them plan to migrate VM workloads within the next two years. 

Kubernetes Talent 

As enterprises adopt Kubernetes, the demand for engineers who excel in the technology is also going to increase, and this will be a big challenge for enterprises, according to Thirumale.

“Kubernetes is not something you are taught in your college. All the learning happens on the job,” he said. “The good news is senior IT managers view Kubernetes and platform engineering as a promotion. So let’s say you’re a VMware admin, storage admin, if you learn Kubernetes and containers, they view you as being a higher-grade person,” he said.

When asked if education institutions in India should start teaching students Kubernetes, he was not completely on board. He believes some basics can be taught as part of the curriculum but there are so many technologies in the world.

“Specialisation happens in the industry; basic grounding happens in the institutions. There are also specialised courses and certification programmes that one can learn beyond one’s college curriculum,” he concluded.

The post How Generative AI is Fueling Demand for Kubernetes appeared first on AIM.

In an exciting development from the xAI team, Grok-2 and Grok-Mini have officially secured positions on the LMSys Chatbot Arena leaderboard. Grok-2 has taken the #2 spot, surpassing GPT-4o (May) and tying with the latest Gemini model, driven by over 6,000 community votes. 

Meanwhile, Grok-2-Mini has earned the #5 position.

Grok-2 has excelled particularly in mathematical tasks, ranking #1 in this category, and secured the #2 positions across various other tasks, including hard prompts, coding, and instruction-following. 

Additionally, Grok-2-Mini has undergone significant speed enhancements, now performing twice as fast as before. This boost was achieved after xAI’s inference team as they completely rewrote the inference stack using SGLang, enabling more efficient multi-host inference and improved accuracy.

The team also introduced new algorithms for computation and communication kernels, alongside better batch scheduling and quantisation, further enhancing the models’ performance.

Several people are still sceptical about the performance. OpenAI’s GPT-4o, which claims the top spot, does not perform as well as Claude 3.5, which is at the 5th spot. Though, people have started experimenting with Grok-2 and claim that the model is actually brilliant in coding and maths related tasks.

Released in Beta this month, the Grok-2 family of models are also available for testing on X. The model also allows users to generate images using the FLUX.1 image generation model.

The post xAI’s Grok-2 Ranks Second on the Chatbot Arena Leaderboard, Competing with Gemini 1.5 and GPT-4o appeared first on AIM.

Unlike typical software development, LLM development is a distinctly different and more complex task, with its own unique set of challenges. One of the most formidable challenges faced by LLM developers is the “curse of multilinguality”. 

Sara Hooker, VP of research at Cohere AI, said, “When you try and make AI actually work for the world, you’re talking about this vast array of different languages. There are 7,000 languages in the world, and 80% of those have no text data.” 

This lack of diverse language data leads to models that overfit high-resource languages like English and Chinese while under-serving the “longtail” of low-resource languages.

It doesn’t stop at that. It gets worse for the reasoning part.

The Elusive Nature of Reasoning

As Subbarao Kambhampati, professor at Arizona State University, illustrates with the classic “manhole cover” interview question, “The tricky part about reasoning is if you ask me a question that requires reasoning and I gave an answer to you, on the face of it, you can never tell whether I memorised the answer and gave it to you or I actually reasoned from first principles.”

Assessing whether an LLM can truly reason rather than just match patterns is difficult. There is often a gap between an LLM’s ability to generate code or text that looks plausible versus its deeper understanding of the underlying logic and ability to reason about it.

Natural language relies heavily on context, shared understanding, and inference to convey meaning. This makes it difficult for LLMs to extract precise semantics and formal logic needed for rigorous reasoning just from language examples.

Furthermore, LLMs have no concept of reality outside of language and cannot test the truth of statements. They are unconcerned about whether concepts contradict each other and only focus on generating sentences that follow language rules.

David Ferrucci, the founder of Elemental Cognition, argues that natural language is insufficient for reliable logical reasoning and computations in complex domains. He states that “for complex reasoning problems where you cannot afford to be wrong, natural language is not the right medium”. 

“Without any underlying formalism, natural language’s ambiguity and subjectivity are great for casually navigating around into another human’s brain, but not the best for ensuring shared meaning and precise, reliable outcomes,” he added.

Ferrucci suggests that formal languages and reasoning systems are needed to enable complex problem-solving.

The Verification Gap

Perhaps the most critical challenge that LLM developers face is the lack of robust methods for verifying the outputs of these models. As Kambhampati notes, “It’s very hard to show what is and what is not on the web,” making it difficult to determine whether an LLM’s output is grounded in factual knowledge or mere hallucination.

A research paper titled ‘TrustLLM: Trustworthiness in Large Language Models’ developed a trustworthiness evaluation framework examining 16 mainstream LLMs across eight dimensions, including fairness, machine ethics, privacy, robustness, safety, truthfulness, accountability, and transparency. 

The researchers found that none of the tested models was truly trustworthy according to their benchmarks, highlighting the need for improved verification methods.

Aidan Gomez, the CEO of Cohere, mentioned that to improve reasoning, language models need to be shown how to break down tasks at a low level, think through problems step-by-step, and have an “inner monologue”. 

“However, data demonstrating this type of reasoning process is extremely scarce on the internet,” he added. 

One of the most significant challenges in verifying the outputs of LLMs is their inherent “black box” nature. LLMs are complex, opaque systems that make it difficult for developers and researchers to understand how they arrive at their outputs.

LLMs suffer from a lack of interpretability, which means it is challenging to understand the reasoning behind their responses. This opacity makes it difficult to identify the root causes of incorrect or inconsistent outputs, hindering efforts to improve the models’ reliability.

Another related issue is the limited explainability of LLMs. Even when an LLM provides an answer, it is often unclear how it arrived at that particular response. This lack of explainability makes it challenging for developers to troubleshoot issues and refine the models.

Addressing the challenges faced by LLM developers will require a multifaceted approach. This includes developing more advanced verification methods to assess the factual accuracy and logical consistency of LLM outputs, improving the interpretability and explainability of LLMs to better understand their inner workings.

By focusing on these key areas, researchers and developers can work towards creating LLMs that are more reliable, trustworthy, and capable of complex reasoning across diverse languages and domains.

The post What is Stopping Devs from Building an LLM? appeared first on AIM.

Technical debt, like financial debt, is a concept in information technology where shortcuts, quick fixes or immature solutions used to meet immediate needs burden enterprises with future costs. This debt can significantly impact supply chain efficiency, especially as businesses face the pressures of staying competitive and agile in a post-pandemic world. 

Madhumita Banerjee, Associate manager, Supply chain and manufacturing at Tredence, sheds light on how technical debt accumulates for Enterprises, and how generative AI can play a pivotal role in addressing these challenges.

Banerjee explained that in the context of supply chains, technical debt accumulates when outdated systems, fragmented processes, and manual, siloed workflows are used. Over time, these inefficiencies lead to increased operational costs, reduced responsiveness, and heightened exposure to risks, making it harder for companies to remain competitive.

One of the primary contributors to technical debt, according to Banerjee, is the reliance on legacy systems. “Many supply chains rely on outdated systems that are difficult to integrate with modern technologies, leading to increased maintenance costs and inefficiencies,” she noted. 

These legacy systems, coupled with data silos where information is stored in disparate systems, create significant barriers to seamless information flow, which is critical for supply chain efficiency.

Manual processes also play a role in accumulating technical debt. Tasks requiring human intervention are prone to errors and delays, contributing to inefficiencies and higher operational costs. 

As companies transitioned to digitalization, the rushed adoption of custom solutions and cloud migrations—often driven by the need to keep pace with technological advancements—introduced added complexity and heightened system maintenance burdens. Generative AI emerges as a pivotal new factor in this scenario. Although early adopters face new risks and the possibility of future debt with each generative AI deployment, the technology shows significant promise in addressing these challenges

The Role of Generative AI in Addressing Technical Debt

Banerjee emphasised that while analytics has historically helped connect data and enhance visibility, the emergence of generative AI, especially LLMs, marked a significant shift. 

“Conversational AI and LLM-powered agents make it easier for functional partners—both technical and non-technical—to understand and act on complex data,” she explained. This is especially crucial in supply chains, where not all stakeholders, such as warehouse partners and freight workers, are tech-savvy.

One of the most significant advantages of generative AI in supply chain management is its ability to enhance data integration and visibility. For instance, in order processing, which traditionally involves many manual steps prone to errors, generative AI can automate the entire workflow—from order intake and validation to order confirmation—ensuring seamless communication across departments and reducing the need for manual intervention.

Generative AI also holds promise in optimising decision-making processes within supply chain platforms. However, Banerjee noted that the effectiveness of generative AI in this area depends on the maturity of the supply chain itself. 

“For instance, if we have an LLM-powered event listener that detects market sentiments and links this information to the forecast engine, it can significantly narrow down the information demand planners need,” she said. 

This level of optimisation requires a robust and connected data model where all data parts communicate effectively, enabling real-time insights and more accurate demand forecasts.

Predictive Analytics, Real-time Data Processing, and Compliance

Banerjee said that predictive analytics is another area where generative AI can revolutionise supply chain management. She recalled the evolution from traditional “what-if” analyses to more advanced machine learning algorithms that predict outcomes over time. 

However, she pointed out that decision-making has now evolved to require not just predictions but also a deeper understanding of cause-and-effect relationships. “With GenAI, we can weave in causal discovery algorithms that translate complex data into actionable insights presented in simple English for all stakeholders to understand,” she added.

This capability is particularly valuable in areas like inventory forecasting, where understanding the root causes of forecast errors and deviations can lead to more accurate and reliable predictions. By translating these insights into easily digestible information, generative AI empowers supply chain managers to make more informed decisions, ultimately improving efficiency and reducing costs.

Speaking about real-time data processing being critical for the effectiveness of generative AI, Banerjee clarified that it’s not the AI that contributes to real-time processing but the other way around. 

“We need to have real-time data to make sure we can analyse scenarios and use generative AI to its maximum potential,” she explained. For instance, ensuring that data entered into legacy systems is immediately available on the cloud allows LLMs to process and convert this data into actionable insights without delay.

In terms of compliance and risk management, generative AI can bolster efforts by removing manual interventions. Banerjee highlighted procurement and transportation as a key area where GenAI can enhance compliance. In transportation, where contracts are reviewed annually, GenAI-powered systems can query specific contracts, compare terms, and ensure adherence to key metrics like freight utilisation and carrier compliance.

Challenges and Future Outlook

Although generative AI offers numerous benefits, challenges still persist. Banerjee stressed the importance of properly vetting the fitment and maturity of Gen AI strategy. “Embarking on the GenAI journey may appear simple, but without a thorough assessment of need and fitment, along with strong investments in data quality, integration, and governance, companies are likely to deepen their technical debt.”, she added

One of the most significant concerns is the issue of “hallucination”, where AI models generate incorrect or misleading information. and validating the data on which AI models are trained to avoid garbage in-garbage out scenarios.

In summary, Banerjee ties the discussion back to the central theme of technical debt. By addressing the key contributors to technical debt—legacy systems, data silos, and manual processes—generative AI can help reduce future costs and risks, enabling companies to pursue digital initiatives with greater confidence. 

“If we can successfully integrate GenAI into our systems, we can revolutionise the entire supply chain platform, making it more efficient, responsive, and competitive”, she concluded.

The post Can Gen AI Reduce the Technical Debt of Supply Chain Platforms appeared first on AIM.

Xiaoyin Qu is not your typical AI startup founder. The super-energetic entrepreneur recently unveiled a new learning app for kids called Heeyo AI. 

Dropping out of Stanford Graduate School of Business to start her entrepreneurial venture and eventually in the learning and teaching space, stands testament to her unwavering commitment to revolutionise education with AI. 

During her late-night discussion with AIM, Qu enthusiastically explained the app, revealing that its vision stemmed from her own childhood desire for a coach who could guide her inventive and curious mind.

“When you are 3 years old, you are just learning to talk. When you are 6 to 7, you can say multiple sentences. When you are 8 or 9, is when you can handle a lot of very open-ended creation. So, that’s why AI actually needs to change the way they talk to you based on your cognitive abilities, and we have both systems that can cater to that,” said Qu, founder and CEO of Heeyo AI, in an exclusive interaction with AIM. 

Defining themselves as ‘smart AI friends that help kids learn,’ California-based Heeyo AI received a $3.5M seed fund from some of the biggest tech players in the market including OpenAI startup fund, Amazon Alexa Fund, Par VC, Charge Ventures, StoryHouse Ventures and other top VCs. The startup is also funded by teams from top universities including Stanford and Harvard. 

Heeyo utilises various AI models, including text-to-speech, speech recognition, text-to-image, and text-to-music. They use specific models like OpenAI and Stable Diffusion for tasks such as content creation, translation, and image generation, with each project involving multiple models in a step-by-step process.

The learning platform provides over 2000+ learning games where the AI figure can speak in over 20 languages through fun avatars such as pandas and dragons.    

Social Emotional Learning 

Heeyo is leveraging its AI platform, which is ideal for 3 to 11 year-olds, to address one of the biggest challenges of social emotional learning in children which is essentially helping kids improve their social skills. 

Qu highlighted the importance of teaching children how to make friends, express themselves, handle rejection, and respond appropriately when meeting someone new, and believes Heeyo will address all that. 

“That’s a relatively new thing that all Silicon Valley parents want to be interested in,” said Qu. 

Furthermore, Heeyo allows anyone including parents, educators, and kids to design their own learning games, and make them based on different cultures. “When you want to do a trivia on some Hindu traditions or Chinese traditions, you can actually build it up. In the US, we see some people are doing, like, Bible stuff. It’s like Bible parenting,” said Qu.

Previous to this, Qu started Run The World, a leading virtual event platform, which she eventually sold last year. She has also led product management at Facebook and Instagram and led marketing & business development for Atlassian’s Asian market. 

AI for the Physical World

Qu is also ecstatic with the kind of funding Heeyo has received, and considers to be very lucky to have OpenAI invested in it. Not just OpenAI, but being backed by Amazon Alexa too, the potential for future products is plenty. 

Qu highlighted how with many children already using Alexa, she believes that their content and interactive experiences could integrate well with Alexa to help even more kids.

“In the long run, we plan to partner with toy companies, like family animals, and some of that. So, that’s going to happen. But, right now, we’re starting more from a content standpoint to make sure we have the right content,” said Qu. 

Interestingly, children’s friendly AI robot Moxie, was also built with the intention of helping children develop social skills. Talking about it, Qu mentioned that Moxie is built for kids with special needs such as autism and has seen many adopt them. However, the price range of it makes it inaccessible for all. 

“We could imagine that Moxie costs $899 and that’s pretty much not affordable for most families,” said Qu, who also said that Heeyo is accessible for all kids. The application is available for free and can be downloaded via the Apple or Google Play store. 

With AI entering the education domain, a number of universities are already tieing up with companies offering AI-related educational services. Recently, Andrej Karpathy, one of the former founders of OpenAI, launched Eureka Labs, which is an AI education company that aims to transform traditional teaching methods with generative AI. 

Furthermore, with AI helping with automating a number of administrative and repetitive tasks, teachers are free’d up for dedicating time for personalised interactions with students, thereby enhancing the quality of education. 

With advanced interactive modes of teaching emerging through AI, learning and even teaching is continuously transforming for the better. 

Heeyo AI not only offers learning games but also allows parents, educators, and kids to design their own games, including those based on specific ethnic cultures and traditions.

The post How a Stanford Dropout’s Startup, Now Backed by OpenAI, is Shaping the Future of Education appeared first on AIM.