NASSCOM Report Unveils Indias Generative AI Startup Landscape 2024

Patent Landscape Report Generative Artificial Intelligence GenAI 5 Patent trends in GenAI applications

As these systems can create original work from patterns in data lakes or question snippets, it’s unclear who owns the rights. In fact, it’s not just about creating more content but shaping unique narratives based on insights that might be overlooked by human minds. This transformative tech opens doors for creatives who need fresh ideas or help to break free from routine approaches.

The continuous advancements in deep learning have been a key driver for the growth of the generative AI market. Deep learning algorithms, especially those based on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have revolutionized the field of generative modeling. GANs have been particularly instrumental in generating realistic and high-quality synthetic data. VAEs, on the other hand, have enabled the creation of latent spaces that facilitate smooth interpolation between data points, allowing for seamless generation of diverse outputs. Sustained Category LeadershipThe best Generative AI companies can generate a sustainable competitive advantage by executing relentlessly on the flywheel between user engagement/data and model performance.

Things in AI are both moving so fast, and getting so much coverage, that it is almost impossible to provide a fully comprehensive “state of the union” of the MAD space, as we did in prior years. We’ve made very few changes to the overall structure of the left side of the landscape – as we’ll see below (Is the Modern Data Stack dead?), this part of the MAD landscape has seen a lot less heat lately. Sign up for our a16z newsletter to get analysis and news covering the latest trends reshaping AI and infrastructure. For example, Microsoft’s Azure-hosted OpenAI has gained significant traction among enterprises, largely due to providing the same control stack as Azure overall.

The business of AI: Big Tech has a head start over startups

And more importantly, this innovation made it so existing generative AI developers could extend their models to other users at an affordable rate. In 2017, Google laid the foundation for the generative AI we use today when the company first proposed a neural network architecture called the Transformer. With transformers, it became possible to create higher-quality language models that could be trained more efficiently and with more customizable features. At this time, tools with predictive text and simple AI chatbots began to emerge and mature sparsely. Like LLMs, AlphaGo was first pre-trained to mimic human experts from a database of roughly 30 million moves from previous games and more from self-play.

8 AI and machine learning trends to watch in 2025 – TechTarget

8 AI and machine learning trends to watch in 2025.

Posted: Fri, 03 Jan 2025 08:00:00 GMT [source]

Video and 3D models are some of the fastest-growing generative AI model formats today. This growth is especially evident in AI video content marketing, which makes use of avatars, audio synthesis, and other generative AI features to create compelling marketing content at scale. By leveraging large language models and platforms like Azure Open AI, for example, organisations can transform outdated code into modern, customised frameworks that support advanced features. As a consequence, these businesses experience increased operational costs and find it difficult to scale or integrate modern technologies. The Chinese Zhejiang University has developed many GenAI patent families in software/other applications, document management and publishing, transportation and security. Among the top research organizations, the Chinese Academy of Sciences (CAS) stands out.

Generative AI solutions use advanced large language models to generate new and original content. NTT DATA offers a range of GenAI services to help businesses leverage the power of AI to drive innovation and growth. From developing custom AI models to integrating world-class models into existing business processes, NTT DATA is helping organizations unlock the full potential of GenAI. Already, marketing teams use it to create ads, email campaigns, and social media posts, and development teams use it in new product development to write software code. Other functions seeing early impact include customer service, where it is used to answer customer questions and resolve complaints; and operations, where it automates tasks and optimizes supply chains. Enterprises are overwhelmingly focused on building applications in house, citing the lack of battle-tested, category-killing enterprise AI applications as one of the drivers.

Factory Unleashes the Droids on Software

Perhaps AI investing, at least when it comes to LLM companies, is going to require mega-sized VC funds – at the time of writing, Saudi Arabia seems to be about to launch a $40B AI fund in collaboration with US VC firms. There are certainly others (marketing, automated SDRs etc) but there’s a lot to figure out (co-pilot mode vs full automation etc). With nearly every vendor calling a foundation model and saying it’s AI, there’s a danger of oversaturation and misleading marketing. This echoes past instances of AI-washing, where everyone who already got burned out on that language in the last generation will once again have to distinguish true innovation from buzzwords. Consequently, CISOs are understandably hesitant to place their trust in solutions that lack concrete evidence of value.

Generative AI tools can be used for simulated attacks and environments, threat intelligence, and synthetic data digital twins of sensitive data. Tech professionals and recreational users alike are becoming familiar with content generation models like ChatGPT, which first emerged in 2022. But this example of generative AI only skims the surface of what generative AI can do and where it’s heading.

• Here’s an overview and a look at what we can expect from one of China’s leading technology companies in the near future.
• C3 Generative AI explains why the AI demand forecast dropped significantly to supply chain teams through interactive chat.
• In visual arts, generative algorithms such as Generative Adversarial Networks (GANs) have been harnessed to produce captivating imagery, ranging from photorealistic landscapes to abstract compositions, often surpassing human imagination.
• However, we continue to believe that there is an essential symbiotic relationship between those areas.
• ChatGPT, used by hundreds of millions of people across the globe, stands as a prominent example of generative AI.
• But some of us don’t fear that AI will replace the human element; at HCG, we think it can empower it.

With ChatGPT, suddenly, you had the experience of interacting with something that felt like an all-encompassing intelligence. We are all routinely exposed to AI prowess in our everyday lives through voice assistants, auto-categorization of photos, using our faces to unlock our cell phones, or receiving calls from our banks after an AI system detected possible financial fraud. But, beyond the fact that most people don’t realize that AI powers all of those capabilities and more, arguably, those feel like one-trick ponies. ChatGPT immediately took over every business meeting, conversation, dinner, and, most of all, every bit of social media. Screenshots of smart, amusing and occasionally wrong replies by ChatGPT became ubiquitous on Twitter.

A New Cohort of Agentic Applications

Over the past couple months, we’ve spoken with dozens of Fortune 500 and top enterprise leaders,2 and surveyed 70 more, to understand how they’re using, buying, and budgeting for generative AI. We were shocked by how significantly the resourcing and attitudes toward genAI had changed over the last 6 months. The global generative AI sector has experienced 5X growth in the number of startups from H1 CY2023 to H1 CY2024, with significant investments, particularly in growth and late-stage funding rounds. Countries across the world are accelerating GenAI adoption and innovation efforts.

Generative AI Landscape: Today’s Trends and Beyond – eWeek

Generative AI Landscape: Today’s Trends and Beyond.

Posted: Wed, 06 Mar 2024 08:00:00 GMT [source]

There are allegations of exploitation of Kenyan workers involved in the data labeling process. Microsoft/GitHub is getting sued for IP violation when training Copilot, accused of killing open source communities. Midjourney might be next (Meta is partnering with Shutterstock to avoid this issue). When an A.I.-generated work, “Théâtre d’Opéra Spatial,” took first place in the digital category at the Colorado State Fair, artists around the world were up in arms.

But, in a highly publicized incident earlier this year, scammers successfully impersonated a company’s CFO and other staff members on a video call using deepfakes, leading a finance worker to send $25 million to fraudulent accounts. Historically, models have been limited by telltale signs of inauthenticity, like robotic-sounding voices or lagging, glitchy video. While today’s versions aren’t perfect, they’re significantly better, especially if an anxious or time-pressured victim isn’t looking or listening too closely. In a recent public warning, the FBI described several ways cybercriminals are using generative AI for phishing scams and financial fraud. For example, an attacker targeting victims via a deceptive social media profile might write convincing bio text and direct messages with an LLM, while using AI-generated fake photos to lend credibility to the false identity.

Thus, overcoming this restraint involves ongoing research into model optimization, efficient parallelization techniques, and cloud-based AI services that can make generative AI more accessible to a broader audience. Reflecting these trends, several strategic movements within the industry highlight the dynamic nature of the market. General Services Administration launched its Generative AI and Specialized Computing Infrastructure Acquisition Resource Guide. This guide, which is regularly updated to reflect technological advancements, aims to assist the federal acquisition community in procuring generative AI solutions and related specialized computing infrastructure.

Implementation alone accounted for one of the biggest areas of AI spend in 2023 and was, in some cases, the largest. One executive mentioned that “LLMs are probably a quarter of the cost of building use cases,” with development costs accounting for the majority of the budget. In order to help enterprises get up and running on their models, foundation model providers offered and are still providing professional services, typically related to custom model development. We estimate that this made up a sizable portion of revenue for these companies in 2023 and, in addition to performance, is one of the key reasons enterprises selected certain model providers. Because it’s so difficult to get the right genAI talent in the enterprise, startups who offer tooling to make it easier to bring genAI development in house will likely see faster adoption.

I’ll also take a look at why the situation for developers of AI platforms is different in China than it is in the West. Baidu has put generative AI to work in a number of other areas designed to improve user experience and generate new business. The traditional software development life cycle (SDLC) is fraught with challenges, particularly requirement gathering, contributing to 40-50% of project failures. A 2024 study found that three-quarters of product features are rarely used, underscoring the need for precision. While traditional AI continues to serve as the predictive and optimization engine of supply chains, generative AI is reshaping how we interact with and analyze data. Traditional AI and generative AI provide different solutions within the supply chain sector.

AI would kill creative jobs last because creativity is the most quintessentially human trait. Another particularly fertile area for generative AI has been the creation of code. In October 2022, CSM (Common Sense Machines) released CommonSim-1, a model to create 3D worlds. In September 2022, OpenAI released Whisper, an automatic speech recognition (ASR) system that enables transcription in multiple languages as well as translation from those languages into English. Also in September 2022, MetaAI released Make-A-Video, an AI system that generates videos from text.

On the basis of type, the market is segmented into text-to-image generation, image-to-image generation, music generation, video generation and 3D modeling and animation. On the basis of application, the market is segmented into gaming, film and television, advertising and marketing, music and sound production and others. Region-wise, it is analyzed across North America, Europe, Asia-Pacific, Latin America, and MEA.

FireTV’s generative AI capabilities are squarely in the entertainment category and Alexa relies on a balance of both. You might ask it for a recipe and cooking instructions or to start your favorite playlist. Consider exploring NVIDIA Broadcast, another AI-powered content creation app that transforms any room into a home studio. The generator attempts to create realistic, lifelike imagery, while the discriminator tries to tell the difference between what’s real and what’s generated. As its neural networks keep challenging each other, GANs get better and better at making realistic-looking samples.

Purpose Built Smaller Foundational Models Commonplace

One industry that has resisted the change but is bound to be significantly transformed is entertainment. Whether lean-back experiences such as music, movies, and television or lean-forward experiences like games, LLMs, and related technologies will enable a shift in the way we think about and consume leisure activities. Broadcom also distinguishes itself from peers by blending semiconductor solutions with software capabilities. Following its VMware merger, Broadcom can seamlessly integrate server storage and networking hardware with a comprehensive infrastructure suite for on-premises and cloud deployments. Substantial software revenue, thanks to VMware, can support margin and free-cash-flow growth, potentially providing investors with a stable, profitable semiconductor investment amidst sector and macro uncertainties.

When we launched the AI 50 almost five years ago, I wrote, “Although artificial general intelligence (AGI)… gets a lot of attention in film, that field is a long way off.” Today, that sci-fi future feels much closer. This year’s AI 50 list shows the dominance of this transformative type of artificial intelligence, which could reshape work as we know it. Our goal with AI is not just to do things better – it’s to do things that were not possible before. Go beyond the hype with expert news on AI, quantum computing, cloud, security and much more.

Revitalizing digital transformation: Harnessing generative AI to improve modern software development.

Predictive AI, on the other hand, uses input data to identify patterns and make forecasts, playing a crucial role in decision-making processes across industries. Understanding these key distinctions is essential for leveraging the respective strengths of each technology. Generative AI excels in creativity and innovation, enabling the generation of unique content, designs, and ideas that can inspire new products and services.

However, Asia-Pacific is expected to exhibit the highest growth during the forecast period. This is attributed to the expansion of advanced technologies such as communication systems, cloud computing, and others, which further contribute to the growth of global market. When most laypeople hear the term generative AI, they think of tools like ChatGPT and Claude powered by LLMs.

The messy real world requires significant domain and application-specific reasoning that cannot efficiently be encoded in a general model. One hypothesis at the outset of the Generative AI market was that a single model company would become so powerful and all-encompassing that it would subsume all other applications. This shift will move us from a world of massive pre-training clusters toward inference clouds—environments that can scale compute dynamically based on the complexity of the task. This is where System 2 thinking comes in, and it’s the focus of the latest wave of AI research. When a model “stops to think,” it isn’t just generating learned patterns or spitting out predictions based on past data. It’s generating a range of possibilities, considering potential outcomes and making a decision based on reasoning.

Achieving artificial general intelligence, that futuristic self-learning system that some fear could threaten humanity, is still a moving target. But there can be no doubt that the progress of large language models over the past year has been transformative—and their applications increasingly general. We’re seeing new use cases every day that demonstrate how AI will change the way we work, create and play. ELB Learning’s Blackmon predicted a rise in personalized generative applications tailored to individual users’ preferences and behavior patterns. For example, a personalized generative music application might create music based on a user’s listening history and mood.

“When you start to get into high-risk applications — things that have the potential to harm or help individuals — the standards have to be way higher,” he said. Although many businesses have explored generative AI through proofs of concept, fewer have fully integrated it into their operations. In a September 2024 research report, Informa TechTarget’s Enterprise Strategy Group found that, although over 90% of organizations had increased their generative AI use over the previous year, only 8% considered their initiatives mature.

Global X Management Company LLC disclaims responsibility for information, services or products found on the websites linked hereto. NVIDIA’s GauGAN, named after post-Impressionist painter Paul Gauguin, is an AI demo for photorealistic image generation. Built by NVIDIA Research, it directly led to the development of the NVIDIA Canvas app — and can be experienced for free through the NVIDIA AI Playground.

We will see the advent of agents going beyond supporting individual needs like writing my email, solving a customer support issue or ordering my groceries to an ecosystem where agents will start to interact with other agents. This paradigm shift revolves closely with the concept of data products, where enterprises will have the opportunity to monetize their agents in the same way they did with their models and datasets. With the reliance on vector databases for generative AI, we will see all the key data platform players to bring to market their flavour for solving this solution. Although vector databases are not a new concept, they are not traditionally part of the “modern data stack” and have been in the past used for search engines and other types of machine learning. For large enterprises it’s a paradigm shift in how they approach problem-solving and innovation, as they move from experimenting to adopting with generative AI. This technological course correction is akin to the transformative wave brought about by the early adoption of cloud technologies, suggesting a similar, if not greater, impact on the tech ecosystem.