5 important things I learned from Generative AI landscape report 2023 from McKinsey by Gaurav Aug, 2023

Generative AI is a subfield of machine learning that involves training artificial intelligence models on large volumes of real-world data to generate new contents (text, image, code,…) that is comparable to what humans would create. This is achieved by training algorithms on large datasets to identify patterns and learn from them. Once the neural network has learned these patterns, it can generate new data that adheres to the same patterns.

• Keep reading to unravel the potential of this technology, how it’s shaping industries, and the layers that make it functional and transformative for end users.
• Generative AI can extract and digitize medical documents to help healthcare providers access patient data more efficiently.
• Despite the obstacles, Intuit’s Hollman said it makes sense for companies that have graduated to more sophisticated ML efforts to build for themselves.
• 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.
• We organized the map by modality, which I thought was most relevant just because it’s the enabling technology that is creating the application within each box.

We provide an overview of over 160 platforms in the space and their investors, as well as insights from leading thought leaders on the potential of this technology. This hands readers a unique opportunity to gain a comprehensive understanding of the generative AI market and the potential for new players to challenge established players like Google. Cohere is set to introduce a new dialogue model to aid enterprise users in generating text while engaging with the model to fine-tune the output. Cohere’s Xlarge model resembles ChatGPT but provides developers and businesses with access to this technology. Cohere’s base model has 52 billion parameters compared to OpenAI’s GPT-3 DaVinci model, which has 175B parameters. Released in February 2023, LLaMA (Large Language Model Meta AI) is a transformer-based foundational large language model by Meta that ventures into both the AI and academic spaces.

Where Generative AI Meets Healthcare: Updating The Healthcare AI Landscape

We can think of Generative AI apps as a UI layer and “little brain” that sits on top of the “big brain” that is the large general-purpose models. There are far more than we have captured on this page, and we are enthralled by the creative applications that founders and developers are dreaming up. For developers who had been starved of access to LLMs, the floodgates are now open for exploration and application development. Machines can analyze a set of data and find patterns in it for a multitude of use cases, whether it’s fraud or spam detection, forecasting the ETA of your delivery or predicting which TikTok video to show you next. Our Window into Progress digital event series continues with „Under the Hood”—a deep dive into the rigor and scale that makes Antler unique as we source and assess tens of thousands of founders across six continents. For the creator economy to succeed, platforms will need to adapt to the creators’ personalities so the creators have some form of connection with their fans when the content may have been mostly supported with AI platforms.

Stability AI plans on monetizing its platform by charging for customer-specific versions. 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.

Mobile Apps

This led to a defining moment with the launch of ChatGPT, the fastest growing app ever, capturing the fascination of creators and users worldwide. Generative AI tools and resources are increasingly available, making this exciting field accessible to many people for the first time. OpenAI’s GPT models are a flavor of transformers that it trained on the Internet, starting in 2018. GPT-3, their third-generation LLM, is one of the most powerful models currently available.

In December 2020, EleutherAI curated a dataset of diverse text for training LLMs called the Pile, which consisted of an 800GiB dataset. EleutherAI also released GPT-J-6B in June 2021, which is a 6 billion parameter language model, making it the largest open-source GPT-3 like model at the time. Additionally, they combined CLIP with VQGAN to develop a free-to-use image generation model, which guided the foundation of Stability AI.

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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.

With the help of generative AI, marketers can produce highly personalized and targeted content at scale. By analyzing vast amounts of data and understanding individual preferences, AI can generate customized messages, advertisements, and product recommendations for each customer, leading to more meaningful and engaging interactions. Generative AI applications assist people in various fields to produce unique and new content. It has a wide variety of applications that are useful for different industries including marketing, advertising, education, communication, and branding. If you want to increase the customer satisfaction of your business, you can create personalized experiences for customers with generative AI tools. In addition, with generative AI, you can analyse your customers’ spending habits and market the product that the customer has the highest purchase potential.

Although these sources enhance the model’s understanding of various tokens, they also introduce the risk of objectionable speech and biases. The model might inadvertently reproduce offensive language, misinformation, or extremist ideologies, reflecting the patterns present in the training data. Moreover, subtler forms of bias can infiltrate LLMs, mirroring societal inequalities. Images speak to us so viscerally, Yakov Livshits and so they’re a lot more fun to share on Twitter than whatever GPT-3 could spit out for me. The GPT acronym means “generative pre-training transformer,” with ChatGPT and other generative AI tools relying on a rigorous training process for the underlying machine learning models. It leverages an internal database laden with phrases and words, facilitating the understanding of language patterns.

Generative AI and Virtual Reality

This capability enables the quick use of an LLM, utilizing billions of stored parameters, to generate the most appropriate response. 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. 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. It uses live conversation intelligence to help frontline teams improve performance and achieve better business outcomes, such as increased sales conversions, improved compliance adherence, and higher customer satisfaction. The platform provides valuable insights into customer conversations, enabling businesses to optimize agent performance, reduce compliance risk, and grow their business.

The rise of data, ML and AI has been one of the most fundamental trends in our generation. Its importance goes well beyond the purely technical, with a deep impact on society, politics, geopolitics and ethics. Yet it is a complicated, technical, rapidly evolving world that can be confusing even for practitioners in the space. There’s a jungle of acronyms, technologies, products and companies out there that’s hard to keep a track of, let alone master.

In the finance sector, generative AI is being used to offer personalized financial services by creating investment portfolios based on customer data and market trends. As the technology continues to advance, we can expect even more innovative uses for Yakov Livshits generative AI in business processes, revolutionizing industries across the board. With the help of chatbots, data analysis and deep learning algorithms, businesses can leverage this technology to create unique content customized to individual users.

This allows developers without extensive AI training to seamlessly integrate AI into their applications, consequently enhancing their functionality and user experience. Frameworks like Hugging Face Transformers, PyTorch Lightning, and TensorFlow Hub significantly improve the accessibility and usability of these models. In addition, they offer libraries of open-source foundation models for various tasks such as text classification, text generation, question answering, and more.