{"id":110901,"date":"2024-02-21T09:05:00","date_gmt":"2024-02-21T17:05:00","guid":{"rendered":"https:\/\/www.backblaze.com\/blog\/?p=110901"},"modified":"2025-12-12T07:17:59","modified_gmt":"2025-12-12T15:17:59","slug":"your-ai-toolbox-16-must-have-products","status":"publish","type":"post","link":"https:\/\/www.backblaze.com\/blog\/your-ai-toolbox-16-must-have-products\/","title":{"rendered":"Your AI Toolbox: 16 Must Have Products"},"content":{"rendered":"\r\n
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Folks, it\u2019s an understatement to say that the explosion of AI has been a wild ride<\/a>. And, like any new, high-impact technology, the market initially floods with new companies. The normal lifecycle, of course, is that money is invested, companies are built, and then there will be winners and losers as the market narrows.<\/a> Exciting times.\u00a0<\/p>\r\n\r\n\r\n\r\n

That said, we thought it was a good time to take you back to the practical side of things. One of the most pressing questions these days is how businesses may want to use AI in their existing or future processes, what options exist, and which strategies and tools are likely to survive long term.\u00a0<\/p>\r\n\r\n\r\n\r\n

We can\u2019t predict who will sink or swim in the AI race\u2014we might be able to help folks predict drive failure<\/a>, but the Backblaze Crystal Ball (\u2122) is not on our roadmap\u2014so let\u2019s talk about what we know. Things will change over time, and some of the tools we\u2019ve included on this list will likely go away. And, as we fully expect all of you to have strong opinions, let us know what you\u2019re using, which tools we may have missed, and why we\u2019re wrong in the comments section.<\/p>\r\n\r\n\r\n\r\n

Tools Businesses Can Implement Today (and the Problems They Solve)<\/h2>\r\n\r\n\r\n\r\n

As AI has become more accessible<\/a>, we\u2019ve seen it touted as either standalone tools or incorporated into existing software. It\u2019s probably easiest to think about them in terms of the problems they solve, so here is a non-inclusive list.<\/p>\r\n\r\n\r\n\r\n

The Large Language Model (LLM) \u201cEverything Bot\u201d<\/h3>\r\n\r\n\r\n\r\n

LLMs are useful in generative AI tasks because they work largely on a model of association. They intake huge amounts of data, use that to learn associations between ideas and words, and then use those learnings to perform tasks like creating copy or natural language search. That makes them great for a generalized use case (an \u201ceverything bot\u201d) but it\u2019s important to note that it\u2019s not the only\u2014or best\u2014model for all AI\/ML tasks.\u00a0<\/p>\r\n\r\n\r\n\r\n

These generative AI models are designed to be talked to in whatever way suits the querier best, and are generally accessed via browser. That\u2019s not to say that the models behind them aren\u2019t being incorporated elsewhere in things like chat bots or search, but that they stand alone and can be identified easily.\u00a0<\/p>\r\n\r\n\r\n\r\n

ChatGPT<\/h4>\r\n\r\n\r\n\r\n

In many ways, ChatGPT is the tool that broke the dam. It\u2019s a large language model (LLM) whose multi-faceted capabilities were easily apparent and translatable across both business and consumer markets. Never say it came from nowhere, however: OpenAI and Microsoft Azure have been in cahoots for years creating the tool that (ahem) broke the internet.\u00a0<\/p>\r\n\r\n\r\n\r\n

Google Gemini, n\u00e9e Google Bard<\/h4>\r\n\r\n\r\n\r\n

It\u2019s undeniable that Google has been on the front lines of AI\/ML for quite some time. Some experts even say that their networks are the best poised to build a sustainable AI architecture<\/a>. So why is OpenAI\u2019s ChatGPT the tool on everyone\u2019s mind? Simply put, Google has had difficulty commercializing their AI product\u2014until, that is, they announced Google Gemini<\/a>, and folks took notice. Google Gemini represents a strong contender for the type of function that we all enjoy from ChatGPT, powered by all the infrastructure and research they\u2019re already known for.<\/p>\r\n\r\n\r\n\r\n

Machine Learning (ML)<\/h3>\r\n\r\n\r\n\r\n

ML tasks cover a wide range of possibilities. When you\u2019re looking to build an algorithm yourself, however, you don\u2019t have to start from ground zero. There are robust, open source communities that offer pre-trained models, community support, integration with cloud storage, access to large datasets, and more.\u00a0<\/p>\r\n\r\n\r\n\r\n