Generative AI Landscape: Current and Future Trends

Stable Diffusion was misused to create an NSFW porn generator, Unstable Diffusion, later shut down on Kickstarter. 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.

Generative AI is a form of artificial intelligence that can generate new data, such as text or images, by learning patterns from its training inputs. Viz.ai is an AI-powered care coordination platform that uses artificial intelligence to connect care teams earlier, ensuring the right patient gets to the right specialist at the right time. It offers a comprehensive AI solution tailored for radiologists to expedite patient diagnosis and treatment, with features such as enhanced alerts, high-fidelity Yakov Livshits mobile and web image viewing, and real-time patient information. The platform coordinates care by connecting frontline healthcare professionals to specialists earlier in the workflow, enabling activation of care teams sooner, and streamlining the consultation process. The platform is HIPAA-compliant, and its text messaging and calling platform empowers clinical teams to conveniently coordinate patient care and treatment decisions in a single hospital and more complex networks.

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This flexibility allows for agile and rapid iterative development, eliminating the need for extensive coding and configuration. Organizations have been faced with and have been working on integration challenges for 60-plus years. Prominent networking technologies for AI workloads, such as InfiniBand and Ethernet, are complemented by high-bandwidth interconnects like NVLink (developed by NVIDIA). Together, these technologies provide solutions that enable connections between both internal and external components of AI clusters. Their coordination ensures efficient data transfer across cloud data centers, with high throughput and minimal latency. Getting an AI to understand context is one of the larger problems with leveraging AI in software development, says Scot Kreienkamp, Senior Systems Engineer at La-Z-Boy.

The exponential acceleration in AI progress over the last few months has taken most people by surprise. It is a clear case where technology is way ahead of where we are as humans in terms of society, politics, legal framework and ethics. For all the excitement, it was received with horror by some and we are just in the early days of figuring out how to handle this massive burst of innovation and its consequences. Its seminal moment, however, came barely five years ago, with the publication of the transformer (the “T” in GPT) architecture in 2017, by Google.

The ethics of generative AI: How we can harness this powerful technology

If you’ve consumed any media in the past few years, you’ve likely seen some AI-generated images, even if you’ve been unaware of them. Once a company gets beyond experimentation and the low-hanging fruit, scaling across the organization becomes the issue. It’s no longer the storage costs that plagued the data boom, but the compute costs of enormous models. Generative AI is also able to deliver live personalization, fairly easily with a company’s existing data he adds. For instance, as a consumer is shopping online, they can ask to see the product in different contexts, new angles, different lighting conditions and more, or even whip up a video on fly. Examples of open source models are Meta’s Llama 2, Databricks’ Dolly 2.0, Stability AI’s Stable Diffusion XL, and Cerebras-GPT.

AI21 is a company focused on revolutionizing Natural Language Processing (NLP) by creating advanced language models that can generate and analyze text. Their technology enables developers to build scalable and efficient applications without requiring NLP expertise. They also offer a writing companion tool called Wordtune that helps users rephrase their writing to say exactly what they mean. Additionally, they offer an AI reader called Read that summarizes long documents for faster comprehension.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

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Application builders may amass this data from in-depth knowledge of an industry or customer needs. For example, consider Harvey, the generative AI application created to answer legal questions. Harvey’s developers fed legal data sets into OpenAI’s GPT-3 and tested different prompts to enable the tuned model to generate legal documents that were far better than those that the original foundation model could create. Once training of this foundational generative AI model is completed, businesses may also use such clusters to customize the models (a process called “tuning”) and run these power-hungry models within their applications. However, compared with the initial training, these latter steps require much less computational power. Observe.AI is an end-to-end AI platform for contact centers that analyzes and provides insights on 100% of customer interactions in real-time.

This all sounds great, but it means lots of new relationships and dependencies among data, programs and organizations. And that’s before considering machine-to-machine API usage, brokered by generative AI. Unmanaged, it means conflicts and confusion with current privacy, risk and compliance frameworks, and hazards such as intellectual property leakage from poorly designed programs released in the wild. The AI itself may be offered back to the developer community as a simple API, creating even more powerful architectures through reusability. There will also be an increase in today’s API collaboration, as people share knowledge, best practices and code snippets, fostering more innovation and yet more code.

What Is the Generative AI Application Landscape?

It took Apple more than two months to reach the same level of adoption for its iPhone. Facebook had to wait ten months and Netflix more than three years to build the same Yakov Livshits user base. GPT-4 is capable of generating natural language responses to prompts, making it possible for users to interact with the system in a conversational way.

ETL, even with modern tools, is a painful, expensive and time-consuming part of data engineering. At the top of the market, the larger players have already been in full product expansion mode. It’s been the cloud hyperscaler’s strategy all along to keep adding products to their platform. Now Snowflake and Databricks, the rivals in a titanic shock to become the default platform for all things data and AI (see the 2021 MAD landscape), are doing the same.

Generative Artificial Intelligence: A New Chapter for Enterprise Business Applications

Content generation models like ChatGPT are becoming more recognizable to both IT experts and laypeople, but this example of generative AI barely scratches the surface of what this technology can achieve and where it’s headed. This IDC Perspective covers the rapidly evolving generative AI applications landscape, approaches to building an enterprise solution, the funding distribution, and insights from CIO quick poll on the promise of generative AI. It also includes generative AI Yakov Livshits basics, benefits, current limitations, and approaches to address those challenges. It provides guidance to the technology buyer on how to embrace generative AI responsibly and maximize ROI. The term “generative” refers to how these models can “generate” new data rather than just analyzing or recognizing existing data. It’s mainly focused on creating authentic-looking artifacts and has found widespread usage in application areas such as art, music, computer vision, and robotics.

• ELB Learning’s Blackmon predicted a rise in personalized generative applications tailored to individual users’ preferences and behavior patterns.
• As of today it’s challenging to see how these platforms identify the original source of truth or where artwork came from – the models are trained by hundreds of millions of data points.
• By analyzing customer data and preferences, generative AI can create personalized content that engages customers at a deeper level.
• Generative AI has become a hot topic in the media and has attracted a lot of investment from venture capitalists and large tech companies.
• In many cases, it may actually enhance the work of creatives by enabling them to create more personalized or unique content, or to generate new ideas and concepts that may not have been possible without the use of AI.