Boom in Large Language Models
The field of natural language processing (NLP) has witnessed an unprecedented boom with the advent of large language models. These models, such as OpenAI’s GPT-4 and its successors, have revolutionized various domains, including text generation, translation, summarization, question-answering, and more.
Large language models have demonstrated an extraordinary ability to understand and generate human-like text. By training on vast amounts of data, these models acquire a comprehensive understanding of grammar, context, and semantics. They excel in generating coherent and contextually relevant responses, allowing them to engage in sophisticated conversations with users. The vast knowledge base they possess contributes to their capability of answering a wide range of questions accurately.
The boom in large language models has unleashed a wave of innovation across industries. In the field of customer service, these models can handle customer queries and provide personalized support at scale, freeing up human agents to focus on more complex tasks. In education, they aid in content creation, generate interactive study materials, and offer personalized tutoring. The healthcare industry benefits from large language models by enabling improved diagnostics, medical research, and telemedicine consultations. Additionally, these models have also found applications in creative writing, legal research, financial analysis, and many other domains.
Generative AI seems to be grabbing all the headlines these days. From articles that explore the vast capabilities of programs like ChatGPT to opinion pieces that question the ethical implications of AI on society, we can’t seem to get away from the topic. Buy, build or partner, this is the question every company is grappling with when it comes to a new technology such as Generative AI.
The first study in a long line of global research examining the effects of generative AI on productivity gains. Erik Brynjolfsson director of Stanford Institute for Human-Centered AI (HAI) is hopeful for what’s down the road. “The fact that we’re getting some really significant benefits suggests that we could have some big benefits over the next few years or decades as these systems are more widely used,”. In the future, generative AI could help bridge economic, societal, and equity gaps in the workplace so everyone benefits. Check out the paper along with the media coverage the study attracted since its release.
The potential of large language models is vast, and their development continues to push the boundaries of what is possible. Future iterations are expected to become more efficient, allowing for faster training and deployment. Customization and fine-tuning capabilities will likely improve, enabling tailored solutions for specific industries and user needs. Collaborative efforts to ensure multilingual and multicultural inclusivity will make these models even more accessible and impactful.
This week focus is on acquisitions. Databricks paid $1.3bn for Generative AI start-up MosaicML MosaicML. provides tools to train and deploy GenAI models, fine tuning with in-house data. This week also brought another news @thomson reuters bought caseText a 10 year old AI company specializing in legal services. CaseText forged a relationship with Openai very early to solve problems that has value to lawyers. Knowing the best prompts that returns good result is enough to justify this acquisition. Likewise Oracle took part in a $270mn investment round in Cohere, a large language modeling start-up this month. Oracle can embed the technology into its own products and its customers. The spate of acqusitions also shows how quickly the opportunity in Generative AI are expanding. Partnerships through equity stakes look like logical first step to bring new technology in house. Earlier this month Salesforce doubled the amount of money it earmaked to invest in AI start-ups as it has ambitious plan to use Generative AI into many of its products. Inflection AI, a one year old AI start-up launched by one of DeepMind’s founders has raised $1.3bn from Microsoft and Nvidia among others, as the surge of investor interest around GenAI grows.
The boom in large language models has brought about a paradigm shift in how we interact with technology and utilize the power of natural language processing. From enhancing customer service experiences to advancing medical care and facilitating global communication, these models have revolutionized numerous industries. As we navigate the ethical challenges and explore further possibilities, large language models hold immense potential to shape a more connected and intelligent future.
