NASSCOM Report Unveils Indias Generative AI Startup Landscape 2024

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

The Republic of Korea shows a relatively high number of GenAI patent families in business solutions, education and agriculture. In relative terms, Japan has a strong research position in entertainment, and arts and humanities. India has an above-average share of all GenAI patent families in networks and smart cities. Germany is in a good research position in physical sciences and engineering and industry and manufacturing.

Users can interact in various contexts, including customer service, language learning, creative writing, and more. The model’s ability to understand and generate human-like text has made it a valuable tool for a wide range of applications. The new generation of AI Labs is perhapsbuilding the AWS, rather than Uber, of generative AI. OpenAI, Anthropic, Stability AI, Adept, Midjourney and others are building broad horizontal platforms upon which many applications are already being created. It is an expensive business, as building large language models is extremely resource intensive, although perhaps costs are going to drop rapidly.

Generative AI Is Exploding. These Are The Most Important Trends To Know

Business units within the enterprise then consume the data product on a self-service basis. A hallmark of the last few years has been the rise of the “Modern Data Stack” (MDS). Part architecture, part de facto marketing alliance amongst vendors, the MDS is a series of modern, cloud-based tools to collect, store, transform and analyze data. Before the data warehouse, there are various tools (Fivetran, Matillion, Airbyte, Meltano, etc.) to extract data from their original sources and dump it into the data warehouse. At the warehouse level, there are other tools to transform data, the “T” in what used to be known as ETL (extract transform load) and has been reversed to ELT (here, dbt Labs reigns largely supreme). After the data warehouse, there are other tools to analyze the data (that’s the world of BI, for business intelligence) or extract the transformed data and plug it back into SaaS applications (a process known as “reverse ETL”).

• They need to address major issues like prompt injection themselves or integrate solutions into the application architecture, but they’re not going to delegate such critical tasks to third parties.
• In other words, they will need to be both narrow and “full stack” (both applications and infra).
• Moreover, the trend is shifting away from relying solely on large, general-purpose models as they are not quite perfect for every need.
• When we say “inference-time compute” what we mean is asking the model to stop and think before giving you a response, which requires more compute at inference time (hence “inference-time compute”).
• Generative AI can help organizations do the right thing, not just do things right.

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MAD 2023, Part II: Financings, M&A and IPOs

From consumer-focused AI applications to enterprise-level solutions, the spectrum of offerings in these marketplaces will cater to a wide array of needs and aspirations. Moreover, the potential launch of ad-sponsored results or media measurement tools by platforms like OpenAI could introduce a new dimension in digital advertising. This development would not only offer new avenues for brand promotion but also challenge existing digital marketing strategies, prompting a reevaluation of metrics and ROI assessment methodologies. Enterprises will navigate a landscape where AI is not only a tool for innovation but also under close regulatory scrutiny. Unified frameworks and standards will emerge, guiding businesses in responsible AI adoption and ensuring that AI’s integration into mainstream society is safe and aligned with public welfare.

Prioritizing these human-centered feedback loops creates living products that continuously improve through real user engagement. Moreover, another significant collaboration occurred in May 2024, when Wipro teamed up with Microsoft to launch a suite of cognitive assistants for the financial services industry, powered by generative AI. These include Wipro GenAI Investor Intelligence, Wipro GenAI Investor Onboarding, and Wipro GenAI Loan Origination. These tools are designed to enhance the efficiency and effectiveness of financial services, showcasing the practical applications of generative AI in transforming business operations. We have already made a number of investments in this landscape and are galvanized by the ambitious founders building in this space.

Google kept its LaMBDA model very private, available to only a small group of people through AI Test Kitchen, an experimental app. The genius of Microsoft working with OpenAI as an outsourced research arm was that OpenAI, as a startup, could take risks that Microsoft could not. At the time of writing, there is a controversy in conservative circles that ChatGPT is painfully woke. A lot of people’s reaction when confronted with the power of generative AI is that it will kill jobs. The common wisdom in years past was that AI would gradually automate the most boring and repetitive jobs.

The Mechanisms Behind Predictive AI Models

Notably, although AI and machine learning talent remains in demand, developing AI literacy doesn’t need to mean learning to code or train models. “You don’t necessarily have to be an AI engineer to understand these tools and how to use them and whether to use them,” Sydell said. The AI model then generates the enhanced image on the other half of the screen in real time. For example, a few triangular shapes sketched using the “mountain” material will appear as a stunning, photorealistic range. Or users can select the “cloud” material and with a few mouse clicks transform environments from sunny to overcast. As models are refined to the point where they can process more data, create higher-resolution media, and accept longer context windows, expect generative AI technology to create immersive experiences that make virtual reality feel real.

Form FactorToday, Generative AI apps largely exist as plugins in existing software ecosystems. Code completions happen in your IDE; image generations happen in Figma or Photoshop; even Discord bots are the vessel to inject generative AI into digital/social communities. Below is a schematic that describes the platform layer that will power each category and the potential types of applications that will be built on top. The necessary conditions for this market to take flight have accumulated over the span of decades, and the market is finally here. The emergence of killer applications and the sheer magnitude of end user demand has deepened our conviction in the market. By contrast, generative AI apps have a median of 14% (with the notable exception of Character and the “AI companionship” category).

These cases spotlight potential infringements where an algorithm has used existing creative material without permission. It has recently launched Generative AI features to provide users with personalized writing suggestions. This is the essence of the paradigm shift — where complexity is no longer the cost of capability.

Although India has made considerable progress, the report ranks it 6th among the top global economies driving the GenAI landscape. Although Baidu doesn’t seem to have stated this explicitly, it’s likely that the idea behind this is that a large, curated database of factual information can help curb the tendency of purely LLM-based models to hallucinate. AI hallucinations happen because LLMs do not actually know anything; they simply construct probabilistic responses based on the text in their training data, which may or may not be factual. The key challenges such as security, bias and accuracy are real and have the potential to derail analytics solutions driven by generative AI. Organizations should assess these challenges as it applies to them and proactively address them. Vivek Bhushan is an AI Solution Director at C3 AI with over a decade of experience at the intersection of supply chain strategy, AI, and technology across various industries.

The vast majority of the organizations appearing on the MAD landscape are unique companies with a very large number of VC-backed startups. A number of others are products (such as products offered by cloud vendors) or open source projects. The landscape is built more or less on the same structure as every annual landscape since our first version in 2012. The loose logic is to follow the flow of data from left to right – from storing and processing to analyzing to feeding ML/AI models and building user-facing, AI-driven or data-driven applications. The onset of these new pricing models and strategies reflects a marketplace that is rapidly adapting to the unique challenges and opportunities presented by AI. As businesses and consumers alike become more familiar with AI capabilities, the demand for flexible, transparent, and value-aligned pricing models will likely intensify.

This tool is specifically targeted at creating artwork and, in particular, is designed to generate traditional Chinese-style ink paintings. In demonstrations, it was shown to be able to understand and interpret traditional Chinese poetry as paintings – a task that is said to be difficult even for most humans. It was recently used to complete an unfinished masterpiece by the renowned traditional Chinese painter Lu Xiaoman, who died in 1965. The second important way that Ernie differs from ChatGPT (or Google’s PaLM, Meta’s Llama or other LLM-based generative AIs) is that it can also create pictures and videos. As such, rather than a large language model, the company refers to its AI technology as an AI Big Model.

Issues of bias and misguided training data can spell disaster for the output of a model. The will be a continued push towards greater accessibility and inclusivity with AI, but challenges remain due to the complexities and costs of developing foundational AI models. This dichotomy sets the stage for increasing public demands for transparency and ethical oversight in AI. Companies with strongholds of data within given verticals like Bloomberg (finance) and LexisNexis (law) are poised to be potential frontrunners in this domain. Bloomberg, with its stronghold in finance data, could introduce sophisticated finance agents and have already started on their own LLMs, while LexisNexis could leverage its vast legal information repository to develop legal agents. These agents, powered by their respective deep moats of data, would not only serve their direct users but also act as invaluable resources for other enterprises and systems to power a new digital workforce.

Our achievement is due to the extensive work done at IBM Consulting®, carefully designing generative AI-based procedures applied across the end-to-end SDLC. We have been adapting and refining our solution for each SDLC stage and task, which allows generative AI to produce consistent and high-quality results. This experience has enabled us to create guided, frictionless procedures adapted to the specific needs of each client to properly address the reality of their SDLC and software landscape. Adoption of generative AI in the end-to-end SDLC brings numerous benefits, such as accelerating development time, improving code quality and reducing costs. It also improves the effectiveness and consistency across tasks and participants by reducing the number of handovers, automating or removing low-value mundane tasks, and facilitating access to knowledge and onboarding. The software development lifecycle has undergone several silent revolutions in recent decades.

Additionally, many make the argument that ChatGPT still requires more work to improve its overall accuracy. • Start with your “why.” Start small and focus on the specific use cases where AI could have the most significant impact on your company. A number of lawsuits have already been filed over the unauthorized use of original works by generative AIs.

Generative AI landscape: Potential future trends – TechTarget

Generative AI landscape: Potential future trends.

Posted: Wed, 19 Apr 2023 07:00:00 GMT [source]

Yee sees a need for regulation that protects the integrity of online speech, such as giving users access to provenance information about internet content, as well as anti-impersonation laws to protect creators. Universities, in contrast, are increasingly offering skill-based, rather than role-based, education that’s available on an ongoing basis and applicable across multiple jobs. “The business landscape is changing so fast. You can’t just quit and go back and get a master’s and learn everything new,” Stave said. “We have to figure out how to modularize the learning and get it out to people in real time.”

The majority of Bytedance’s GenAI patents are in the fields of software/other applications and document management and publishing. However, to note, a large number of patent families cannot be assigned to a specific application and are instead included in the category software/other applications. Based on our analysis of GenAI patents, we have identified the applications where research activities are focused on. The following list shows the 21 application areas identified, ranked according to the number of published patent families within the last decade. A short description of current GenAI trends within these applications including patent examples is included in the Appendices.

The high computational complexity presents a challenge for small and medium-sized enterprises and individual developers who may not have the financial means or infrastructure to invest in such hardware. Moreover, researchers and developers have made significant progress in refining these algorithms, optimizing model architectures, and introducing new techniques to stabilize training and enhance the quality of generated content. As deep learning continues to evolve, the capabilities of gen AI models are expected to improve further, leading to even more impressive and realistic results. Simultaneously, the emergence of AI-as-a-Service (AIaaS) platforms is democratizing access to generative AI technologies.