14 Aug Generative AI Landscape: Applications, Models, Infrastructure

How Generative AI Will Transform the Marketing Landscape

End-to-end applications in the realm of generative AI are comprehensive software solutions that employ generative models to provide specific services to end users. Such applications typically include proprietary machine learning models that a particular company has developed and owns. They encapsulate these models within a user-friendly interface, concealing the intricate technicalities of the underlying AI. Large language models (LLMs) like OpenAI’s GPT-4 and Google’s PaLM 2 are specific closed source foundation models that focus on natural language processing. They have been fine-tuned for applications like chatbots, such as ChatGPT and Bard. A non-language example is OpenAI’s DALL-E 2, a vision model that recognizes and generates images.

While Google’s actual release of generative AI tools has been delayed, its dedication to extensive testing and AI ethics implies that its planned solutions will be strong and successful when they are ultimately published. With the DreamzAR app, homeowners can create their designs at a fraction of the cost. They can experiment with as many designs as they like until they find the one that best suits their needs. One notable observation is the significant rise Yakov Livshits of generative AI startups, which account for an impressive 22% of YC’s Winter 2023 batch, with 59 out of 272 startups. This surge in numbers is even more remarkable when compared to the combined total of the past five years, which saw just 45 such startups emerge between 2017 and 2022. Is generative AI that once-every-15-years kind of generational opportunity that is about to unleash a massive new wave of startups (and funding opportunities for VCs)?

Accelerating AI Deployment with MLOps, Generative AI Models, and LlamaIndex

In these models, GPUs can concurrently execute typical operations like matrix multiplication, resulting in a significantly faster training process than a traditional CPU (Central Processing Unit). Generative AI models are developed to generate new content based on the patterns they learn from vast training datasets. However, given the size and complexity of these datasets, the process of training generative AI models is both computationally intensive and storage demanding. To overcome these challenges, AI practitioners leverage the power of cloud computing platforms, which provide the necessary resources without substantial investment in local hardware. Platforms like Midjourney and Runway ML exemplify tools that enable the creation of end-to-end applications utilizing proprietary models in the generative AI context. Midjourney empowers developers to construct, deploy, and scale AI applications, offering them a set of tools to leverage AI technologies without necessarily being experts in machine learning or data science.

• And just as the inflection point of mobile created a market opening for a handful of killer apps a decade ago, we expect killer apps to emerge for Generative AI.
• Though generative AI systems based on large language models (LLMs), such as OpenAI’s extremely popular ChatGPT, may seem like sudden technological breakthroughs, these have been several years in the making.
• The integration of generative AI in industries promises to reshape the future of work and revolutionize how we interact with technology.

Before that, her byline was featured in SF Weekly, The Nation, Techworker, Ms. Magazine and The Frisc. Stay updated on the latest developments and breakthroughs in generative AI to capitalize on new opportunities and remain competitive in the market. Below, we’ll review all the different ways B2B marketing teams can integrate generative AI into their workflows. For B2B marketers, Yakov Livshits this is a huge opportunity to leverage one of the latest tools available to level up your strategies. Discover the beauty, energy, and insight of AI creations in visual art, music, and poetry. Learn how artists using AI are pushing creativity to new levels to create modern masterpieces, impact the fashion industry, and discover new forms of expression at GTC.

Advantages to a Platform Strategy for Generative AI

We are overdue for an update to our MAD Public Company Index, but overall, public data & infrastructure companies (the closest proxy to our MAD companies) saw a 51% drawdown compared to the 19% decline for S&P 500 in 2022. Many of these companies traded at significant premiums in 2021 in a low-interest environment. In 2022, both public and private markets effectively shut down and 2023 is looking to be a tough year.

Companies can also create carefully refined marketing profiles and therefore, finely tune their services to the specific need. Open Banking platforms like Klarna Kosma also provide a unique opportunity for businesses to overlay additional tools that add real value for users and deepen their customer relationships. Generative AI can create bots capable of performing various tasks, such as customer service, marketing, and data analysis.

Despite Generative AI’s potential, there are plenty of kinks around business models and technology to iron out. Questions over important issues like copyright, trust & safety and costs are far from resolved. PaLM variants scale up to 540 billion parameters (vs GPT-3 at 175 billion) and trained on 780 billion tokens (vs GPT-3 300bn) — totalling around 8x more compute training than GPT-3 (but likely considerably less than GPT-4). Being a dense decoder-only Transformer model, PaLM is trained on two TPU V4 pods connected over a data center network and uses a combination of model and data parallelism. This large TPU configuration allows for efficient scale training without using pipeline parallelism.

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.

Pharmaceutical companies use generative AI to optimize drug discovery and development processes. AI-driven models analyze vast datasets to identify potential drug candidates, predict drug interactions, and simulate molecular structures. This streamlines the drug development pipeline, leading to faster and more cost-effective pharmaceutical research. You can compose landscape design entirely using provided design elements or switch to AI Landscape Design Stylist and ask it to generate more designs based on your work.

There was some stuff on the internet that wasn’t that good, and so I literally put it in OpenAI, “the difference between classical AI and generative AI,” and it started spitting out amazing stuff. It wasn’t just a joke that the article was co-written with GPT-3; it actually was. And then I’d be like, “Specifically for image generation, you can think of it as ….” That human-machine iteration loop I hadn’t experienced before, and it was very much how we created both the blog post and landscape. When it comes to using generative AI for marketing purposes, it’s clear that adoption of this technology is inevitable, but it should be leveraged responsibly. By harnessing the power of generative AI, B2Bs can significantly enhance their creative capabilities, streamline content creation processes, and craft compelling narratives that resonate with target audiences.

Content Generation & Seo

NVIDIA Instant NeRF is an inverse rendering tool that turns a set of static 2D images into a 3D rendered scene in a matter of seconds by using AI to approximate how light behaves in the real world. This active space has numerous companies addressing the pain points of prior authorization, a process where clinicians or pharmacies must obtain special approval for equipment, drugs, or surgeries (often expensive). Companies in this sector range from long-standing firms like Rhyme to newer upstarts like Latent Health. Even Doximity has entered the field, offering a chat-GPT tool to help draft appeal letters. Many of these companies target specialty pharmacy due to the growing trend of these drugs and their high costs. With incumbents and innovators competing against each other, as well as AI/software distributors who may choose to upsell, this space is interesting to watch.

Up until recently, machines had no chance of competing with humans at creative work—they were relegated to analysis and rote cognitive labor. But machines are just starting to get good at creating sensical and beautiful things. This new category is called “Generative AI,” meaning the machine is generating something new rather than analyzing something that already exists. GitHub Copilot is a tool that assists developers in programming by using AI to convert natural language into coding suggestions. It is powered by OpenAI Codex, which allows it to understand the developer’s coding style and suggest context-specific solutions.

Introduction to Generative AI, co-authored by Numa Dhamani and Maggie Engler, is your compass in navigating this complex terrain. Challengers like Oracle have made inroads with big capex expenditures and sales incentives. And a few startups, like Coreweave and Lambda Labs, have grown rapidly with solutions targeted specifically at large model developers. They also expose more granular resource abstractions (i.e. containers), while the large clouds offer only VM instances due to GPU virtualization limits.

Lastly, Bloom is an open-access multilingual language model, containing 176 billion parameters and was trained on 384 A100–80GB GPUs. Anthropic has developed a conversational large language model AI chatbot named Claude, which uses a messaging interface and a technique called constitutional AI to better align AI systems with human intentions. AnthropicLM v4-s3 is a 52-billion-parameter, autoregressive model, trained unsupervised on a large text corpus. The ten principles used by Anthropic are based on the concepts of beneficence, non-maleficence, and autonomy. Claude is capable of a variety of conversational and text-processing tasks, such as summarization, search, creative and collaborative writing, Q&A, and coding. It is easy to converse with, more steerable, and takes directions on personality, tone, and behavior.

PaLM excelled in 28 out of 29 NLP tasks in the few-shot performance, beating the prior larger models like GPT-3 and Chinchilla. This made it far easier to interact with these LLMs and to get them to answer questions and perform tasks without getting sidetracked by just trying to predict the next word. A fortunate feature of instruction tuning is that not only it helps to increase the accuracy and capabilities of these models, but they also help align them to human values and helps prevent them from generating undesired or dangerous content. There are several key players in the generative AI space, each offering different approaches and solutions.