2023 data, ML and AI landscape: ChatGPT, generative AI and more

While generative AI will likely affect most business functions over the longer term, our research suggests that information technology, marketing and sales, customer service, and product development are most ripe for the first wave of applications. In all cases, application developers will need to keep an eye on generative AI advances. The technology is moving at a rapid pace, and tech giants continue to roll out new versions of foundation models with even greater capabilities. OpenAI, for instance, reports that its recently introduced GPT-4 offers “broader general knowledge and problem-solving abilities” for greater accuracy.

Overall, the impact of Gen-AI is sure to be significant, as it has the potential to enable the creation of new and useful content and to improve the performance of machine learning systems. Image Generation is a process of using deep learning algorithms such as VAEs, GANs, and more recently Stable Diffusion, to create new images that are visually similar to real-world images. Image Generation can be used for data augmentation to improve the performance of machine learning models, as well as in creating art, generating product images, and more. Text Generation involves using machine learning models to generate new text based on patterns learned from existing text data. The models used for text generation can be Markov Chains, Recurrent Neural Networks (RNNs), and more recently, Transformers, which have revolutionized the field due to their extended attention span.

The modern data stack under pressure

Workers could interact with the NLI through a headset connected to a manufacturer’s ERP system to navigate a packed warehouse, find specific items, and reorder materials and supplies. TXI’s Chekal sees the potential for generative AI to improve patient outcomes and make life easier for healthcare professionals. Generative AI can extract and digitize medical documents to help healthcare providers access patient data more efficiently. Yakov Livshits It will also improve personalized medicine and therapeutics by organizing more medical, lifestyle and genetic information for the appropriate algorithms. Intelligent transcription will save time and help summarize complex information as part of doctor-patient conversations rather than as a separate process. It will also improve patient engagement through personalized recommendations, medication reminders and better symptom tracking.

Your business no doubt spends a lot of time and money to purchase or develop content such as knowledge assessments and training modules. By taking advantage of generative AI applications, you could produce content faster and at less cost, and release it in more frequent cycles. There are great possibilities for using a custom-built generative AI tool to help improve your company’s cybersecurity awareness program. And in this post, we look at five ways your organization might do that, now or in the future.

• Just as mobile unleashed new types of applications through new capabilities like GPS, cameras and on-the-go connectivity, we expect these large models to motivate a new wave of generative AI applications.
• Users may expect these plagiarism-detecting programs to change as educational issues increase.
• It provides guidance to the technology buyer on how to embrace generative AI responsibly and maximize ROI.
• As with AI in general, dedicated generative AI services will certainly emerge to help companies fill capability gaps as they race to build out their experience and navigate the business opportunities and technical complexities.

The majority of today’s generative AI models have time-based and linguistic limitations. As generative AI grows in demand around the world, more and more of these vendors will need to make sure their tools can accept inputs and create outputs that align with various linguistic and cultural contexts. Similarly to when classroom technologies have changed in the past — overhead projectors, anyone?

MAD 2023, part I: The landscape

Consider OpenAI’s GPT-3 and GPT-4, foundation models that can produce human-quality text. They power dozens of applications, from the much-talked-about chatbot ChatGPT to software-as-a-service (SaaS) content generators Jasper and Copy.ai. Generative models are used in a variety of applications, including image generation, natural language processing, and music generation. They are particularly useful for tasks where it is difficult or expensive to generate new data manually, such as in the case of creating new designs for products or generating realistic-sounding speech. 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. A non-language example is OpenAI’s DALL-E 2, a vision model that recognizes and generates images.

Cohere is a language AI platform that offers a user-friendly API and platform to power multiple use cases for global companies. Their large language models enable powerful capabilities such as content generation, summarization, and search at a massive scale. Their high-performance, secure, and customizable language models work on public, private, or hybrid clouds to ensure data security and exceptional support. Cohere’s generative AI tools allow users to write product descriptions, blog posts, articles, and marketing copy.

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.

These cutting-edge Gen-AI platforms will undoubtedly support and enhance our daily lives, but it will take time for us to fully adapt to them. Gen-AI is being used in gaming in a number of ways, including to create new levels or maps, to generate new dialogue or story lines, and to create new virtual environments. For example, a game might use a Gen-AI model to create a new, unique level for a player to explore each time they play, or to generate new dialogue options for non-player characters based on the player’s actions. Additionally, Gen-AI can be used to create new, realistic virtual environments for players to explore, such as cities, forests, or planets. Overall, it can be used to add a level of dynamism and variety to gaming experiences, making them more engaging and immersive for players.

“Enterprises need to embrace generative AI responsibly partnering with trusted suppliers and collaboratively addressing the existing concerns and challenges.” Generative AI can automate specific tasks that are currently done by humans, freeing up time so you can focus on more creative and strategic work. Companies like Jasper, launched almost two years ago, reportedly generated nearly $100 million in revenue and a $1.5 billion valuation. Similarly, OpenAI, the company behind GPT-3 and other AI models, is rumored to raise funds at a valuation in the tens of billions of dollars.

Generative AI landscape: Potential future trends

This year, particularly given the explosion of brand new areas like generative AI, where most companies are 1 or 2 years old, we’ve made the editorial decision to feature many more very young startups on the landscape. The has made significant strides, with various industries benefiting from their advanced capabilities. To combat students’ tendency to rely on ChatGPT and similar tools to do their homework, teachers can use one of the many free AI content plagiarism detectors that have now emerged. Though they’re not foolproof, these tools are able to effectively estimate what percentage of content has been artificially generated.

Likely due to the capital-intensive nature of developing large language models, the generative AI infrastructure category has seen over 70% of funding since Q3’22 across just 10% of all generative AI deals. Most of this funding stems from investor interest in foundational models and APIs, MLOps (machine learning operations), and emerging infrastructure like vector database tech. Data augumentation is a process of generating new training data by applying various image transformations such as flipping, cropping, rotating, and color jittering.

How do the training models work in practice?

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”).

Overall, AI21 aims to transform reading and writing into AI-first experiences and empower users to be better versions of their writing and reading selves. Advancements in deep learning techniques and access to large datasets will lead to even more realistic and creative content generation. Ethical AI practices will gain prominence, focusing on mitigating biases and ensuring transparency in AI decision-making. Additionally, interdisciplinary integration with other AI technologies will result in powerful synergies and new applications across industries such as healthcare, education, and entertainment. Generative AI has many promising apps that span across a variety of industries, including chatbots and data analysis. With the help of deep learning algorithms, generative AI can analyze vast amounts of data to generate new content in various forms such as images, videos or music.

The particularly impressive second version, DALL-E 2, was broadly released to the public at the end of September 2022. With transformers, one general architecture can now gobble up all sorts of data, leading to an overall convergence in AI. We highlighted the data mesh as an emerging trend in the 2021 MAD landscape and it’s only been gaining traction since.