Despite the monumental leaps in artificial intelligence (AI) we’ve witnessed in recent years, the prospect of Artificial General Intelligence (AGI)—machines possessing the ability to understand, learn, and perform any intellectual task precisely as a human can—remains a far-off goal. Yes, advancements have been made with tools like GPT-4, Alpha Go, and Gato, contributing to the foundation of AGI. However, these are still considered early examples of AGI and not fully formed AGI systems.
Introduction
In data science and machine learning, cosine similarity is a measure that calculates the cosine of the angle between two vectors. This metric reflects the similarity between the two vectors, and it’s used extensively in areas like text analysis, recommendation systems, and more. This post delves into the intricacies of implementing cosine similarity checks using TypeScript.
Understanding Cosine Similarity and Its Applications in AI
Cosine similarity measures two non-zero vectors of an inner product space. It is defined as the cosine of the angle between them, which is calculated using this formula:
Picture this: It’s 3 AM, and you’re staring at your computer screen, bleary-eyed, as you struggle to solve that pesky bug in your code. You can almost feel the weight of the digital cobwebs piling up on StackOverflow as you sift through outdated answers and snarky comments. But what if I told you that the days of scouring through StackOverflow’s seemingly endless abyss might be numbered? Enter ChatGPT, Google Bard, and a whole new breed of AI-powered chatbots revolutionising how developers find answers to their coding conundrums.
In the past year, a surge of AI tools has hit the market, with many identifying as AI startups. The advent of OpenAI’s ChatGPT, including GPT-3.5 and GPT-4 models, has revolutionised how we interact with technology. However, amidst this excitement, a trend needs addressing: the phenomenon of “API wrappers” masquerading as AI startups.
While it’s true that many of these products utilize the power of OpenAI’s GPT APIs, it’s essential to take a step back and consider the implications of relying solely on an external API for your business. Does wrapping GPT APIs and selling a service based on them warrant the label of an AI startup? Let’s take a closer look at the potential downsides of this approach.
It’s no secret that artificial intelligence is booming. With the advent of ChatGPT in 2022, AI is becoming a hot topic. Naturally, many developers are also increasingly interested in building with AI, and there is no shortage of resources to learn from and reference.
There are a couple of glaring roadblocks of sorts right now. Most AI tutorials and resources are focused on Python and vector databases like Pinecone or Supabase PostgreSQL. These are great options, and I recommend learning them, but what about the TypeScript/Javascript and Node.js crowd who wants to experiment?
The recent call by OpenAI for the US government to consider licensing and registration requirements for AI with specific capabilities has stirred up a mix of emotions and concerns among AI enthusiasts, including myself.
CEO Sam Altman argues that regulation is essential for maintaining safety standards. Still, there is a valid concern that this move could create a corporate stronghold around AI, stifling open-source AI tools and models.
The Push for AI Regulation: Weighing the Pros and Cons
GitHub Copilot Chat is a tool that brings a chat interface to the editor, focusing on developer scenarios and natively integrating with VS Code and Visual Studio. It is built upon OpenAI and Microsoft’s work with ChatGPT and the new Bing. However, despite its promising premise, the current version of Copilot Chat leaves much to be desired.
A lot of the technical prowess from the team that incorporated GPT-4 into Bing Chat also allegedly went into GitHub Copilot Chat. Although, it’s hard to shake the feeling that Copilot Chat feels like it has some artificial brakes applied at the moment. Possibly due to its controlled and limited release.
In the ever-evolving digital landscape, AI-powered chatbots like ChatGPT have become increasingly popular. With a staggering 100 million users in just two months, ChatGPT’s growth has been nothing short of extraordinary. Although AI chatbots offer numerous benefits, they may also be contributing to a concerning trend: AI Dependence Syndrome.
In this blog post, we’ll explore the potential dangers of relying too heavily on AI chatbots and how this dependence can impact our ability to learn and grow in both our professional and personal lives.
Despite being a major player in the tech world and a pioneer in AI research, Google has left me scratching my head with their latest AI chat assistant, Google Bard. With ChatGPT from OpenAI setting a high bar in the AI space, other tech behemoths have been trying to replicate its success, especially in the wake of the post-pandemic tech slump and worsening global economic conditions. However, Google’s recent offering seems a step in the wrong direction.
In recent years, we’ve seen an unprecedented rise in the development and adoption of artificial intelligence (AI) tools, such as OpenAI’s ChatGPT. As AI becomes increasingly integrated into our daily lives, it’s essential to ask whether governments should step in and regulate these technologies.
While some argue that ChatGPT is a glorified sentence constructor and poses no real threat, others believe that regulation is necessary to prevent misuse and ensure ethical practices. In this article, we’ll explore both perspectives and attempt to determine whether AI regulation is needed.