Harshil Mathur-led Indian payments gateway platform Razorpay has been included in the Forbes Cloud 100 list for 2024. This is the company’s third consecutive appearance on the Forbes list of the top 100 cloud-computing private companies in the world. The list was released on August 6, 2024.
Razorpay remains the only Indian company featured on the list, along with global AI giants such as Open AI, Databricks, Stripe and Canva.
“Being featured on the Forbes Cloud 100 list for the third time is an incredible honour for us at Razorpay. To be the only Indian company on this prestigious list is not just a proud moment for us, but a testament to the potential and impact of India’s financial technology sector on the global stage,” said Harshil Mathur, chief executive officer and co-founder of Razorpay.
Razorpay ranks as the 70th entrant on the Forbes Cloud 100 list of companies, which highlights companies that are AI challengers and standouts in the fintech industry.
The payment gateway company has nearly 3,300 employees and has raised close to $742 million in funding from its Series A to Series F rounds, according to the list. Razorpay raised money from investors such as Lone Pine Capital, Alkeon Capital, TCV, GIC, Tiger Global, Sequoia, Capital India, Ribbit Capital, Matrix Partners, Salesforce Ventures, Y Combinator, and MasterCard.
The companies featured on the list have received funding from Bessemer Venture Partners and Salesforce Ventures, and the company data collected for the list was compiled through their help, according to the Forbes list methodology.
“The evaluation process involved four factors: estimated valuation (30 per cent), operating metrics (20 per cent), people and culture (15 per cent), and market leadership (35 per cent), which the judging panel then weighed to select, score, and rank the winners,” reported PTI, quoting a Newsvoir press release.
What’s next for Razorpay?
Razorpay estimates that the Digital P2M Payments industry in India will continue to grow and reach $4 trillion by 2030, according to the press release.
“Recognizing the ever-expanding potential for startups, freelancer, and enterprises, the company will continue to invest in building an intelligent real-time financial infrastructure, supported by next-gen AI technologies to help businesses scale and meet their ever-evolving payment and banking needs,” said the company in the statement.
Razorpay is a private startup co-founded by Harshil Mathur and Shashank Kumar. This year’s list highlights companies that have harnessed the power of AI and strategically positioned themselves for potential IPOs and market leadership.
Google became one of the world’s largest companies by building the world’s most popular internet search engine. But the advent of AI has spawned an army of rivals ready to upstage its monopoly. Now, with OpenAI showcasing SearchGPT, should Google be scared?
Is SearchGPT really novel?
Not really. Microsoft’s Bing—incidentally powered by OpenAI’s generative pre-trained transformer (GPT) foundational AI models—has already showcased the practical uses of a generative AI-driven search and browsing experience. SearchGPT remains within that ambit, but seeks to improve it by ensuring that the AI search algorithm remembers queries for follow-ups. As a result, SearchGPT has so far been advertised as an early-stage experiment to see how generative AI might be fitted into commercial search products. This would be key to see how the future of search can be monetized by Big Tech.
Can Google keep pace?
Well before SearchGPT, Google had unveiled a Search Generative Experience as an internal test product. And in May, it expanded the scope of new features on AI-powered search that uses its latest AI model—Gemini. The essence of Google’s AI-powered search experience is the same as that of SearchGPT. However, the key difference is that while Google has dominated the search space so far, competitors with similar interfaces and algorithmic prowess could out-muscle Google in an industry that it monopolizes globally. To be sure, neither Google’s nor OpenAI’s new search platform is openly available yet.
Who are the other competitors?
Bing is perhaps the best known among Google and OpenAI competitors. Another is the startup Perplexity AI, backed by Nvidia and Jeff Bezos, among others. Smaller, independent competitors include privacy-centred browser Brave’s AI search feature, You.com’s AI search, Komo, Phind and Waldo. But none have the deep pockets of Google, Microsoft and OpenAI.
Can this change how we use the internet?
Yes. A big change will come in the way search and targeted ads work. Today, search engine service providers track internet activity and serve ads based on your usage. In return, they earn commissions from advertisers. In a chatbot-like platform, this changes due to the interface design. This could make a huge impact: over half of Google parent Alphabet’s annual revenue each year comes from Search. For users, the big change could be finding new websites—AI chat interfaces could more closely control information sources.
Why should Google be worried?
Between April and June, Google earned $48.5 billion from its search business. Safe to say, much of its core business is dependent on search. The AI search race could be won by whoever has the better, more powerful AI model. OpenAI’s GPT-4o, Meta’s Llama 3.1 and Anthropic’s Claude 3.5 Sonnet are in line to challenge and outperform Google’s Gemini 1.5 Pro in search today. Google has user stickiness and repute on its side—while OpenAI is new, it’s unlikely to oust Google, which has 30 years of dominance, overnight.
PWC, a professional-services firm, reckons that AI could add almost $16trn to global economic output by around 2030 (compared with 2017). McKinsey, a consulting firm, separately arrived at a similar figure, but now reckons this could rise by another 15-40% because of newer forms of AI such as large language models. Yet Africa, which has around 17% of the world’s population, looks likely to get a boost from AI in its annual GDP of just $400m by 2030, or 2.5% of the total, because it lacks digital infrastructure. As a result, instead of helping to narrow the productivity and income gap between Africa and richer countries, AI seems set to widen it.
Take Nigeria, a regional tech hub whose average download speed of wired internet is a tenth of Denmark’s. Most broadband users in Africa’s most populous country are limited to mobile internet, which is slower still. A growing number of underwater cables connect the continent with the wider world, with more to come. These include Meta’s 2Africa, the world’s longest undersea connection. But a dearth of onshore lines to carry data inland will leave much of that capacity wasted.
In some ways Africa’s weak digital infrastructure is explained by the success of its mobile revolution, whereby privately owned telcos entered newly liberalised markets, disrupting and displacing the incumbent operators. These not-so-new firms are still growing rapidly—the 15 main ones have averaged 29% revenue growth over the past five years. But their jump over landlines is coming back to bite them. In much of the rich world, the basic infrastructure of telephones—junction boxes and telephone poles or underground cable conduits—have been repurposed to provide fast fibre-optic broadband. Yet Africa is often starting from scratch.
The lack of connectivity is compounded by a shortage of the heavy-duty data centres needed to crunch the masses of data required to train large language models and run the AI-powered applications that could boost Africa’s economic growth. These days much of the content and processing needed to keep websites and programmes running is held in the cloud, which is made up of thousands of processors in physical data centres. Yet Africa has far fewer of these than any other major continent (see map).
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(The Economist)
Without nearby data centres, bits and bytes have to make long round-trips to centres in cities such as Marseille or Amsterdam for processing, leading to lagging applications and frustrating efforts to stream high-definition films. Yet the closer data are to users, the faster they can reach them: films can zip across to viewers from one of Netflix’s African servers more quickly than you can say “Bridgerton”. The more cable landings and more local data centres there are on the continent, the more resilient its network is if undersea cables are damaged, as happened earlier this year when internet access was disrupted across much of west Africa.
All these new data centres will require more energy as they grow. AI, which involves complex calculations that need even more computing power, will further raise demand. A rack of servers needed for AI can use up to 14 times more electricity than a rack of normal servers. They also need industrial air-conditioning, which guzzles massive amounts of power and water—even more so in ever-hotter climates.
Yet Africa is so short of electricity that some 600m of its people have no power. In Nigeria, which suffers 4,600 hours of blackouts a year, data centres are forced to provide their own natural gas-powered generating plants to keep the lights on and the servers humming. Though many centres across the continent are turning to renewables, wind and solar are too erratic to do the job continuously.
Edge computing, where more data is processed on the user’s device, is promoted as a way to bring AI-powered tech to more Africans. But it relies on the presence of many smaller and less energy-efficient data centres, and on users having smartphones powerful enough to handle the calculations. Though around half of mobile phones in Africa are now smartphones, most are cheap devices that lack the processing power for edge computing.
In 18 of the 41 African countries surveyed by the International Telecommunication Union, a minimal mobile-data package costs more than 5% of average incomes, making them unaffordable for many. This may explain why almost six in ten Africans lack a mobile phone, and why it is not profitable for telcos to build phone towers in many rural areas. “Approximately 60% of our population, representing about 560m people, have access to a 4G or a 3G signal next to their doorstep, and they’ve never gone online,” says Angela Wamola of GSMA, an advocacy group for mobile operators. Every next yet-to-be-connected African is more expensive to reach than the last, and brings fewer returns, too. And new phone towers in remote areas, which typically cost $150,000 each, still need costly cables to “backhaul” data.
Part of the solution to Africa’s connectivity problem may be partnerships between mobile-phone operators and development institutions. Existing telcos know the terrain and the politics that can make laying cables a delicate task. International tech firms such as Google or Microsoft are well placed to take on more risk by laying their own cables and building data centres. Equipment-providers and other multinationals can fill skill gaps.
China’s Huawei, for example, is building 70% of Africa’s 4G networks. Startups using cheaper technologies are exploring how to help far-flung communities get connected. Africa’s connectivity mix will probably be as diverse as its people, including everything from satellites that can be put up by firms like Starlink to reach rural areas, to improved 4G networks in medium-sized cities.
Some foreign firms are investing in data centres in Kenya and Nigeria, but not enough of them. There is also some experimenting with how to power them. Kenya’s Ecocloud Data Centre, for example, will be the continent’s first to be fully run on geothermal energy, a stable source of renewable power. Since Kenya’s grid has plenty more green energy available, it is an attractive place to build more data centres.
But given how many power sources your correspondent switched between to write this article, and how many patchy internet connections interrupted her work, much still needs to be done to improve infrastructure. That is even truer if Africa’s animators, weather forecasters, quantum physicists and computer scientists are to fulfil their potential. Even small-scale farming, which provides a living for more than half the continent’s people, stands to benefit from improved access to AI.
Frustratingly, the case for improving Africa’s digital infrastructure is not new. “Gosh! I can’t believe, 15 years later, we’re still having this conversation,” says Funke Opeke, whose firm, MainOne, built Nigeria’s first privately owned submarine cable in 2010. Unless big investments are made soon, the same conversation may be taking place another 15 years on.
While this is the obvious part, beneath the surface, the bigger fight is also about controlling all streams of user data, including those from search engines and social media, which can help big tech companies such as Google, OpenAI, Microsoft, Meta, Nvidia and Elon Musk’s xAI build the world’s most powerful artificial intelligence (AI) model.
ChatGPT managed to garner more than 100 million users in just the first two months of its launch in December 2023, prompting many to dub it a search-engine killer. The reason was that ChatGPT allows us to write poems, articles, tweets, books, and even code like humans and is interactive, while search engines passively provide article links. Microsoft, which has a stake in OpenAI, even integrated ChatGPT with its own search engine, Bing. At that time, though, ChatGPT was still being tested and lacked knowledge of current events, having trained on data only till the end of 2021.
From September 2023, ChatGPT began accessing the internet, thus providing up-to-date information. But it started facing allegations of “verbatim”, “paraphrase”, and “idea” plagiarism and copyright violations from publishers around the world. Late last year, for instance, The New York Times initiated legal proceedings against Microsoft and OpenAI, alleging unauthorized “copying and using millions of its articles”. OpenAI did give publishers the option to block bots from crawling their content but separating AI bots from those originating from search engines such as Google or Microsoft’s Bing, which facilitate page indexing and visibility in search outcomes, is easier said than done.
OpenAI’s SearchGPT prototype, which is currently available for testing, will not only access the web but also provide “clear links to relevant sources”, the company said in a blog post on 26 July. This implies that more than targeting Google’s search engine, OpenAI appears to be trying to pacify and rebuild rapport with publishers it has antagonised. And this time around, OpenAI is “…also launching a way for publishers to manage how they appear in SearchGPT, so publishers have more choices”.
It clarifies that SearchGPT is about search and “separate from training OpenAI’s generative AI foundation models”. It adds that the search results will show sites even if they opt out of generative AI training. OpenAI explains that a webmaster can allow its “OAI-SearchBot to appear in search results while disallowing GPTbot to indicate that crawled content should not be used for training OpenAI’s generative AI foundation models”.
Equations are changing, but slowly
To be sure, ChatGPT’s success is already making a dent in Google’s worldwide lead, which makes most of its revenue from advertising. For instance, Google saw its smallest search market share on desktops registered in more than a decade. Microsoft’s Bing, which supported and integrated ChatGPT into its service, surpassed 10% of the market share on desktop devices, according to Statista.
Google, whose advertising search revenue was $279.3 billion in 2023, is taking a hit, with many users already preferring Generative AI (GenAI) for searching online information first. “Many companies heard the call and saw $13 billion invested in generative AI (GenAI) for broad usage, namely search engines and large language models (LLMs), in 2023,” according to Statista.
Yet, Google, according to Statista, continues to control more than 90% of the search-engine market worldwide across all devices, handling over 60% of all search queries in the US alone and generating over $206.5 billion in ad revenues from its search engine and YouTube. In India, too, the search-engine giant has a market share of over 92%, but in countries like Germany and France, though, online users are increasingly choosing “privacy- or sustainability-focused alternatives such as DuckDuckGo or Ecosia”, according to Statista. China, on its part, has Baidu, while South Korea favours Naver; even Russia’s Yandex now has the third-largest market share among search engines worldwide.
ChatGPT certainly did not topple Google, agrees Dan Faggella, founder of market research firm Emerj Artificial Intelligence Research. “But it (OpenAI) definitely was seemingly their strongest real competitor,” he adds. “I’m much more nervous for Perplexity in, say, the next three months than I am about Google,” says Fagella, for the lack of a “differentiator”.
“I think it’s a cool app. But I wonder if there’s enough of a context wrap for things like enterprise search. Google used to do enterprise search but no longer sees sense in it,” he adds. Perplexity, which has raised $100 million from the likes of Amazon founder Jeff Bezos and Nvidia, was valued at $520 million in its last funding round.
In a February interview with Mint, Srinivas argued that while Google will continue to have a “90-94% market share”, they will lose “a lot of the high-value traffic—from people who live in high-GDP countries and earning a lot of money, and those who value their time and are willing to pay for a service that helps them with the task”. He argued that over time, “the high-value traffic will slowly go elsewhere”, while low-value “navigational traffic” will remain on Google, making Google “a legacy platform that supports a lot of navigation services”.
“The bigger consideration is that the means and interfaces through which search occurs are evolving. These may become new interfaces other than the Chrome tab, where Google can very much get pushed aside, and I think the VR (virtual reality) ecosystem will be part of that as well. I don’t see Google dying tomorrow. But I think they should be shaking in their boots a little bit around what the future of search will be,” says Fagella.
Race to dominate the AI space
Fagella believes that “search is a subset of a much broader substrate monopoly game. It’s all about owning the streams of attention and activity—from personal and business users for things like their workflows, personal lives and conversations to help them (big tech companies) build the most powerful AI”. This, he explains, is why all big companies want you to have their chat assistant so that they can continue to economically dominate.
Fagella believes that all the moves indicate that the big tech companies, including Google, Meta, and OpenAI, “are ardently moving towards artificial general intelligence (AGI). “Apple’s a little quieter about it. I don’t know where Tim Cook stands. They’re always a little bit more standoffish. But suffice it to say, they’re probably in that same running as well, although seemingly not as overt about it,” he adds.
OpenAI, for instance, has multimodal GenAI models, including GPT-4o and GPT-4 Turbo, while Google’s Gemini 1.5 Flash is available for free in more than 40 languages. Meta recently released Llama 3.1 with 405 billion parameters, which is the largest open model to date, and Mistral Large 2 is a 128 billion-parameter multilingual LLM. Big tech companies are also marching ahead on the path to achieve AGI, which envisages AI systems that are smarter than humans.
OpenAI argues that because “…the upside of AGI is so great, we do not believe it is possible or desirable for society to stop its development forever; instead, society and the developers of AGI have to figure out how to get it right…We don’t expect the future to be an unqualified utopia, but we want to maximize the good and minimize the bad and for AGI to be an amplifier of humanity”.
And OpenAI does not mind spending a lot of money to pursue this goal. The ChatGPT maker could lose as much as $5 billion this year, according to an analysis by The Information. However, in a conversation this May with Stanford adjunct lecturer Ravi Belani, Sam Altman said, “Whether we burn $500 million a year, or $5 billion or $50 billion a year, I don’t care. I genuinely don’t (care) as long as we can, I think, stay on a trajectory where eventually we create way more value for society than that, and as long as we can figure out a way to pay the bills like we’re making AGI it’s going to be expensive it’s totally worth it,” he added.
In July, Google DeepMind proposed six levels of AGI “based on depth (performance) and breadth (generality) of capabilities”. While the ‘0’ level is no AGI, the other five levels of AGI performance are: Emerging, competent, expert, virtuoso and superhuman. Meta, too, says it’s long-term vision is to build AGI that is “open and built responsibly so that it can be widely available for everyone to benefit from”. Meanwhile, it plans to grow its AI infrastructure by the end of this year with two 24,000 graphics processing unit (GPU) clusters using its in-house designed Grand Teton open GPU hardware platform.
Elon Musk’s xAI company, too, has unveiled the Memphis Supercluster, underscoring the partnership between xAI, X and Nvidia, while firming up his plans to build a massive supercomputer and “create the world’s most powerful AI”. Musk aims to have this supercomputer—which will integrate 100,000 ‘Hopper’ H100 Nvidia graphics processing units (and not Nvidia’s H200 chips or its upcoming Blackwell-based B100 and B200 GPUs)—up and running by the fall of 2025.
What can spoil the party
No AI model to date can be said to have powers of reasoning and feelings as humans do. Even Google DeepMind underscores that other than the ‘Emerging’ level, the other four AGI levels are yet to be achieved. LLMs, too, remain highly advanced next-word prediction machines and still hallucinate a lot, prompting sceptics like Gary Marcus, professor emeritus of psychology and neural science at New York University, to predict that the GenAI “…bubble will begin to burst within the next 12 months”, leading to an “AI winter of sorts”.
“My strong intuition, having studied neural networks for over 30 years (they were part of his dissertation) and LLMs since 2019, is that LLMs are simply never going to work reliably, at least not in the general form that so many people last year seemed to be hoping. Perhaps the deepest problem is that LLMs literally can’t sanity-check their own work,” says Marcus.
I elaborated on these points in my 19 July newsletter, Misplaced enthusiasm over AI Appreciation Day. When will AI, GenAI provide RoI?, where Daron Acemoglu, institute professor at the Massachusetts Institute of Technology (MIT), argues that while GenAI “is a true human invention” and should be “celebrated”, “too much optimism and hype may lead to the premature use of technologies that are not yet ready for prime time”. His interview was published in a recent report, Gen AI: too much spend, too little benefit?, by Goldman Sachs.
There’s also the fear that all big AI models will eventually run out of finite data sources like Common Crawl, Wikipedia and even YouTube to train their AI models. However, a report in The New York Times said many of the “most important web sources used for training AI models have restricted the use of their data”, citing a study published by the Data Provenance Initiative, an MIT-led research group.
“Indeed, there is only so much Wikipedia to vacuum up. It takes billions of dollars to train this thing, and you’re going to suck that up pretty quickly. You’re also going to start sucking up all the videos pretty quickly, despite how quickly we can pump them in,” Fagella agrees.
He believes that the future of AI development will involve integrating sensory data from real-world interactions, such as through cameras, audio, infrared, and tactile inputs, along with robotics. This transition will enable AI models to gain a deeper understanding of the physical world, enhancing their capabilities beyond what is possible with current data.
Fagella points out that the competition for real-world data and the strategic deployment of AI in robotics and life sciences will shape the future economy, with major corporations investing heavily in AI infrastructure and data acquisition, even as data privacy and security will remain critical issues. He concludes, “The inevitable transition is to be touching the world.”
In a stunning display of mathematical prowess, Google’s AI systems, AlphaProof and AlphaGeometry 2, have achieved silver medal-level performance at the prestigious International Mathematical Olympiad (via India Today).
AlphaProof, a groundbreaking AI system introduced by Google, excels in formal mathematical reasoning, reported the publication. Utilizing a blend of language models and the AlphaZero reinforcement learning algorithm—renowned for mastering chess and Go—AlphaProof trains itself to tackle complex math problems using Lean, a formal language for mathematics. Demonstrating its capabilities, AlphaProof successfully solved two challenging algebra problems and one number theory problem during the IMO, including the competition’s most difficult problem, a feat achieved by only five human contestants.
Reportedly, the second AI system, AlphaGeometry 2, is a notable advancement over Google’s earlier geometry-solving AI. Using a neuro-symbolic hybrid method, it integrates an advanced language model with a robust symbolic engine.
This enhancement enabled AlphaGeometry 2 to solve intricate geometry problems more efficiently. During the IMO, AlphaGeometry 2 impressively solved Problem 4 in just 19 seconds, which involved complex geometric constructions and a deep understanding of angles, ratios, and distances. Trained on a vast dataset encompassing 25 years of historical IMO geometry problems, AlphaGeometry 2 boasts an impressive 83 per cent success rate in solving these challenges.
Google’s AI systems achieved a score of 28 out of 42 points at the IMO, falling just one point short of a gold medal. Renowned mathematicians, such as Fields Medal recipient Prof Sir Timothy Gowers and Dr. Joseph Myers, Chair of the IMO 2024 Problem Selection Committee, reviewed the AI’s solutions. They concluded that the AI could produce impressive and non-obvious solutions, highlighting a significant milestone in AI’s ability to perform complex mathematical reasoning.
This achievement underscores Google’s progress in advancing AI technology, with the potential to revolutionize various fields by assisting mathematicians in exploring new hypotheses, solving longstanding problems, and automating time-consuming elements of mathematical proofs.
In the future, Google intends to share additional technical information about AlphaProof and to further investigate various AI methodologies to improve mathematical reasoning, adds the publication. Their goal is to create AI systems that collaborate with human mathematicians, thereby advancing the frontiers of science and technology.
After months of rumours, Sam Altman’s startup OpenAI has finally unveiled a search engine competitor to Google called SearchGPT. The new feature is currently in ‘prototype’ stage and is only available via a waiting list, but is expected to be rolled out to all users in the future.
In a blogpost about new search feature, OpenAI wrote, “We’re testing SearchGPT, a prototype of new search features designed to combine the strength of our AI models with information from the web to give you fast and timely answers with clear and relevant sources.”
Also Read | Meta prioritizes open-source play, native Hindi support to rival OpenAI, Google
SearchGPT start page is akin to Google and we get a message reading, “what are you looking for?” After entering the search query, though, you get a direct answer much like Perplexity or Google’s disgraced AI overviews feature.
A query for music festivals in Boone, Northern California in August returns a list of all such festivals, along with a 2-3 line description that prominently mentions the source from which the information was taken. Users are also given a links option on the left-hand side of the page, where they can view all the links cited by OpenAI and open them for more detailed information. In addition, similar to ChatGPT, users can ask follow-up questions to get more information.
OpenAI, which is already being sued by major news publishers like The New York Times, said that it is committed to a thriving ecosystem of publishers and creators. The company said SearchGPT uses AI to highlight high quality content in a conversational interface while providing user the opportunity to connect with news publishers via the cited links.
ANYONE CAN spot a tipping point after it’s been crossed. Also known as critical transitions, such mathematical cliff-edges influence everything from the behaviour of financial markets and the spread of disease to the extinction of species. The financial crisis of 2007-09 is often described as one. So is the moment that covid-19 went global. The real trick, therefore, is to spot them before they happen. But that is fiendishly difficult.
Computer scientists in China now show that artificial intelligence (AI) can help. In a study published in the journal Physical Review X, the researchers accurately predicted the onset of tipping points in complicated systems with the help of machine-learning algorithms. The same technique could help solve real-world problems, they say, such as predicting floods and power outages, buying valuable time.
To simplify their calculations, the team reduced all such problems to ones taking place within a large network of interacting nodes, the individual elements or entities within a large system. In a financial system, for example, a node might represent a single company, and a node in an ecosystem could stand for a species. The team then designed two artificial neural networks to analyse such systems. The first was optimised to track the connections between different nodes; the other, how individual nodes changed over time.
To train their model, the team needed examples of critical transitions for which lots of data were available. These are hard to find in the real world, because—cue circular logic—they are so hard to predict. Instead, the researchers turned to simplified theoretical systems in which tipping points are known to occur. One was the so-called Kuramoto model of synchronised oscillators, familiar to anyone who has seen footage of out-of-sync pendulums beginning to swing together. Another was a model ecosystem used by scientists to simulate abrupt changes, such as a decline in harvested crops or the presence of pests.
When the researchers were happy that their algorithms could predict critical transitions in these systems, they applied them to the real-world problem of how tropical forests turn to savannah. This has happened many times on Earth, but the details of the transformation remain mysterious. Linked to decreased rainfall, this large-scale natural switch in vegetation type has important implications for any wildlife living in the region, as well as the humans who depend on it.
The researchers got hold of more than 20 years of satellite images of tree coverage and mean annual rainfall data from central Africa and identified the times at which three distinct regions transitioned from tropical forest to savannah. They then wanted to see if training their algorithm on data from two of these regions (with each node standing in for a small area of land) could enable it to correctly predict a transition point in the third. It could.
The team then asked the algorithm to identify the conditions that drove the shift to savannah—or, in other words, to predict an oncoming phase transition. The answer was, as expected, down to annual rainfall. But the AI was able to go further. When annual rainfall dropped from 1,800mm to 1,630mm, the results showed that average tree cover dropped by only about 5%. But if the annual precipitation decreased from 1,630mm to about 1,620mm, the algorithm identified that average tree cover suddenly fell by more than 30% further.
This would be a textbook critical transition. And by predicting it from the raw data, the researchers say they have broken new ground in this field. Previous work, whether with or without the assistance of AI, could not connect the dots so well.
Like with many AI systems, only the algorithm knows what specific features and patterns it identifies to make these predictions. Gang Yan at Tongji University in Shanghai, the paper’s lead author, says his team are now trying to discover exactly what they are. That could help improve the algorithm further, and allow better predictions of everything from infectious outbreaks to the next stockmarket crash. Just how important a moment this is, though, remains difficult to predict.
In an interview on the sidelines of the Google I/O Connect held in Bengaluru on Wednesday, Ajjarapu reasoned that with its largest mobile-first population, micro-payment and digital payment models, a booming startup and developer ecosystem, and diverse language landscape, “India is uniquely positioned to drive the next generation of AI innovation.”
In India, Google works with the Ministry of Electronics and Information Technology’s Startup Hub to train 10,000 startups in AI, expanding access to its artificial intelligence (AI) models like Gemini and Gemma (family of open models styled on Gemini tech), and introducing new language tools from Google DeepMind India, according to Ajjarapu.
It supports “eligible AI startups” with up to $350,000 in Google Cloud credits “to invest in the cloud infrastructure and computational power essential for AI development and deployment.”
Karya, an AI data startup that empowers low-income communities, is “using Gemini (also Microsoft products) to design a no-code chatbot,” while “Cropin (in which Google is an investor) is using Gemini to power its new real-time generative AI, agri-intelligent platform.”
Manu Chopra, co-founder and CEO of Karya, said he uses Gemini “to take Karya Platform global and enable low-income communities everywhere to build truly ethical and inclusive AI.”
Gemini has helped Cropin “build a more sustainable, food-secure future for the planet,” according to Krishna Kumar, the startup’s co-founder and CEO.
Robotic startup Miko.ai “is using Google LLM as a part of its quality control mechanisms,” says Ajjarapu.
According to Sneh Vaswani, co-founder and CEO of Miko.ai, Gemini is the “key” to helping it “provide safe, reliable, and culturally appropriate interactions for children worldwide.”
Helping farmers
With an eye on harnessing the power of AI for social good, Google plans to soon launch the Agricultural Landscape Understanding (ALU) Research API, an application programming interface to help farmers leverage AI and remote sensing to map farm fields across India, according to Ajjarapu.
The solution is built on Google Cloud and on partnerships with the Anthro Krishi team and India’s digital AgriStack. It is piloted by Ninjacart, Skymet, Team-Up, IIT Bombay, and the Government of India, he pointed out.
“This is the first such model for India that will show you all field boundaries based on usage patterns, and show you other things like sources of water,” he added.
On local language datasets, Ajjarapu underscored that Project Vaani, in collaboration with the Indian Institute of Science (IISc), has completed Phase 1 — over 14,000 hours of speech data across 58 languages from 80,000 speakers in 80 districts. The project plans to expand its coverage to all states of India, totaling 160 districts, in phase two.
Project Vaani introduced IndicGenBench, a benchmarking tool tailored for Indian languages, which covers 29 languages. Additionally, Project Vaani is open-sourcing its CALM (Composition of Language Models) framework for developers to integrate specialised language models with Gemma models. For example, integrating a Kannada specialist model into an English coding assistant may help in offering coding assistance in Kannada as well.
Google, which has Gemini Nano tailored for mobile devices, has introduced the Matformer framework, developed by the Google DeepMind team in India. According to Manish Gupta, director, Google, it allows developers to mix different sizes of Gemini models within a single platform.
This approach optimises performance and resource efficiency, ensuring smoother, faster, and more accurate AI experiences directly on user devices.
India-born Ajjarapu was part of Google’s corporate development team that handled mergers and acquisitions when Google’s parent Alphabet acquired UK-based AI company DeepMind in 2014. As a result, he got the opportunity to conduct the due diligence and lead the integration of DeepMind with Google.
Research, products and services
Ajjarapu, though, was not a researcher, and was unsure of meaningfully contributing to DeepMind’s mission, which “at that time, was to solve intelligence.” This prompted him to quit Google in 2017 after 11 years, and launch Lfyt’s self-driving division. Two years later, Ajjarupu rejoined Google DeepMind as senior director, engineering and product.
Last year, Alphabet merged the Brain team from Google Research and DeepMind into a single unit called Google DeepMind, and made Demis Hassabis its CEO. Jeff Dean, who reports to Sundar Pichai, CEO of Google and Alphabet, serves as chief scientist to both Google Research and Google DeepMind.
While the latter unit focuses on research to power the next generation of products and services, Google Research deals with fundamental advances in computer science across areas such as algorithms and theory, privacy and security, quantum computing, health, climate and sustainability and responsible AI.
Has this merger led to a more product-focused approach at the cost of research, as critics point out? Ajjarapu counters that Google was still training its Gemini foundation models when the units were merged in April 2023, after which it launched the Gemini models in December, followed by Gemini 1.5 Pro, “which has technical breakthroughs like a long context window (2 million tokens that covers about 1 hour of video, or 11 hours of audio, or 30,000 lines of code).”
A context window is the amount of words, known as tokens, a language model can take as input when generating responses.
“Today, more than 1.5 million developers globally use Gemini models across our tools. The fastest way to build with Gemini is through Google AI Studio, and India has one of the largest developer bases on Google AI Studio,” he notes.
Google Brain and DeepMind, according to Ajjarapu, were also collaborating “for many years before the merger”.
“We believe we built an AI super unit at Google DeepMind. We now have a foundational research unit, which Manish is a part of. Our team is part of that foundation research unit. We also have a GenAI research unit, focused on pushing generative models regardless of the technique — be it large language models (LLMs) or diffusion models that gradually add noise (disturbances) to data (like an image) and then learn to reverse this process to generate new data,” said Ajjarapu, who is part of the product unit and whose job is to “take the research and put it in Google products.”
Google also has a science team, which is primarily responsible for things like protein folding and discovering new materials. Protein folding refers to the problem of determining the structure if a protein from its sequence of amino acids alone.
“There are many paradigms to go after AI development, and we feel like we’re pretty well covered in all of them,” he says. “We’re now fully in our Gemini era, bringing the power of multimodality to everyone.”
Match, incubate and launch
And how does Google decide which research products and product ideas to prioritise and invest in? According to Ajjurupa, the company uses an approach called “match, incubate, and launch.”
Is there a problem that’s ready to be solved with a technology that’s readily available? That’s the matching part. For instance, for graph neural nets, the map is a graph. So there is a match. However, even if there’s a match, performance is not guaranteed when it comes to generative AI.
“You have to iterate it,” he says.
The next step involves de-risking an existing technology or research breakthrough for the real world since not all of them are ready to be made into products. This phase is called incubation. The final stage is the launch.
“That’s the methodical approach that we follow. But given the changing nature of the world, and changing priorities, we try to be nimble,” says Ajjarupu.
Gupta, on his part, asks his research team to identify research problems that will have “some kind of a transformative impact on the world, which makes it worthy of being pursued, even if the problem is very hard or the chances of failure are very high.”
And how is Google DeepMind addressing ethical concerns around AI, especially biases and privacy? According to Gupta, the company has developed a framework to evaluate the societal impact of technology, created red teaming techniques, data sets and benchmarks, and shared them with the research community.
He adds that his team contributed the SeeGULL dataset (benchmark to detect and mitigate social stereotypes about groups of people in language models) to uncover biases in language models based on aspects such as nationality and religion.
“We work to understand and mitigate these biases and aim for cultural inclusivity too in our models,” says Gupta.
Ajjarapu adds that the company’s focus is on “responsible governance, responsible research, and responsible impact.”
He cited the example of the Google SynthID — an embedded watermark and metadata labelling solution that flags photos (deepfakes) generated using Google’s text-to-image generator, Imagen.
OpenAI has introduced a five-tier system to measure its progress toward developing artificial intelligence (AI) capable of surpassing human performance, reported Bloomberg. This move aims to provide clearer insight into the company’s approach to AI safety and its vision for the future. The classification system was unveiled to employees during an all-hands meeting, an OpenAI spokesperson confirmed.
Reportedly, the tiers range from the current conversational AI (Level 1) to advanced AI that can operate an entire organization (Level 5). OpenAI, widely regarded as a frontrunner in the quest for more powerful AI systems, plans to share these levels with investors and other external stakeholders.
Currently, OpenAI considers itself at the first level, but nearing the second, known as “Reasoners.” This stage refers to AI systems capable of basic problem-solving tasks comparable to a human with a doctorate, but without access to additional tools.
During the same meeting, OpenAI’s leadership showcased a research project involving its GPT-4 model, demonstrating new capabilities that exhibit human-like reasoning.
As per Bloomberg, an insider who requested anonymity, mentioned that OpenAI continually tests new functionalities internally, a standard practice in the AI industry.
OpenAI has long aimed to create artificial general intelligence (AGI), which entails developing computers that outperform humans on most tasks. Although AGI does not currently exist, CEO Sam Altman has expressed optimism that it could be achieved within this decade. The criteria for reaching AGI have been a topic of debate among AI researchers.
In November 2023, researchers at Google DeepMind proposed a five-level framework for AI, including stages such as “expert” and “superhuman,” akin to the system used in the automotive industry for self-driving cars. OpenAI’s newly introduced levels also feature five ascending stages towards AGI. The third level, “Agents,” refers to AI systems capable of performing tasks over several days on behalf of users. The fourth level involves AI that can generate new innovations, and the highest level, “Organizations,” signifies AI that can operate autonomously within an organization.
These tiers were developed by OpenAI’s executives and senior leaders and are still considered a work in progress. The company intends to collect feedback from employees, investors, and its board to refine the levels further.
Billionaire Elon Musk revived a lawsuit against OpenAI, giving rise to the six-year-old dispute that started with a power struggle at the San Francisco-based start-up, reported the New York Times on Monday, August 5.
The new lawsuit, filed on Monday in a Northern Californian federal court, claimed that OpenAI and two of its founders, Sam Altman and Greg Brockman, broke the company’s contract, which puts commercial interest ahead of the public good. This is similar to the claim of the original lawsuit, as per the report.
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Elon Musk withdrew his original lawsuit seven weeks ago. This move came without any explanation from Musk before the judge was about to give a verdict.
Sam Altman and Brockman created OpenAI with Elon Musk in 2015 and promised to develop artificial intelligence for the benefit of humanity, as per the lawsuit. Then, allegedly, both the founders deserted the mission when the firm entered a multibillion-dollar partnership with Microsoft, according to the report.
Musk was “betrayed by Mr. Altman and his accomplices,” said the lawsuit. Open AI did not immediately respond to the New York Times’ request for comments.
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Altman responded to Musk’s original lawsuit by saying that they intended to ask that its claims be dismissed and the company aims to cater to the public good by developing an artificial general intelligence (A.G.I.) machine capable of doing anything that the human brain is capable of, as per the report.
“The mission of OpenAI is to ensure A.G.I. benefits all of humanity, which means both building safe and beneficial A.G.I. and helping create broadly distributed benefits,” they said.
Musk separated his ties with OpenAI in 2018 after a power struggle in the company, which was created with a vision to freely share artificial intelligence technology with the public, as it was too dangerous to be controlled by a single entity like Google, according to the report. Open AI was converted into a for-profit company and it also raised $13 billion from tech giant Microsoft.
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The OpenAI board fired Sam Altman in November 2023, stating that the founder could no longer be trusted with the company’s mission of building artificial intelligence for the benefit of humanity. According to the report, he returned to the company after five days.
Musk sued OpenAI two months after that incident. The new case alleges that the two founders have misled Musk when they created OpenAI, according to the report.
“Elon Musk’s case against Sam Altman and OpenAI is a textbook tale of altruism over greed,” said the lawsuit. “Altman, in concert with other defendants, intentionally courted and deceived Musk, preying on Musk’s humanitarian concern about the existential dangers posed by AI,” said the lawsuit quoted in the news report.
Musk filed the new lawsuit on Monday as OpenAI allegedly violated federal racketeering laws by conspiring against Musk to defraud him, the New York Times quoted Marc Toberoff, Musk’s lawyer, as saying.
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“The previous suit lacked teeth — and I don’t believe in the tooth fairy,” said Toberoff. “This is a much more forceful lawsuit,” he said as per the report.
As per the lawsuit, OpenAI’s contract with Microsoft states that the company will not have any rights over OpenAI’s technology once the lab has achieved A.G.I., according to the report. The lawsuit also questioned whether or not the A.G.I has been achieved to determine whether the Microsoft contract should be voided or not, said the report.
OpenAI is worth more than $80 billion, according to the company’s latest funding round. Industry experts also say that OpenAI’s current technology is not A.G.I. and that the scientists do not know how to build such a system, as per the report.
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HomeAIArtificial IntelligenceElon Musk moves court against OpenAI again; Sam Altman and Greg Brockman sued: previous case ‘lacked teeth’, says lawyer
