--- # System prepended metadata title: 10 Factors That Influenced the Rapid Rise of AI --- # 10 Factors That Influenced the Rapid Rise of AI While artificial intelligence is now part of our everyday lives, it didn't just suddenly appear out of thin air. It wasn't a single invention that put things into motion. Yet, it did grow fast, due to a couple of factors intervening. You could call it a mixture of economic, technical, and social elements. Together they've created the fuel that gave power to rapid AI development. While there were many factors that helped the rise of AI, those we listed below hold the top spots in our book.

The Data Explosion

AI is all about learning. It requires data to feed itself. Thanks to our everyday modern life, data is everywhere. Messages you sent? Data. Search queries on yahoo, Google, Bing and other search engines? All data. The same goes for photos, videos and every piece of data even you manufacture during the day. Most of the appliances we use daily such as smartphones, laptops, smart TVs, and different apps all generate data continuously. We don't even need to go into the huge volume of data generated on social media platforms as we speak. Before technology sparked the data revolution, the creation of that information was scarce. Collecting data was expensive, and individuals and companies only preserved what they deemed necessary. Today, every piece of data is stored. Cloud storages are reaching the point where there's no limit to how much can be preserved. Everything we mentioned means the world to AI. With first language models introduced the amount of written data it could consume was massive, and it grew daily. It quickly moved to photos, videos, and even voices. The rise of artificial intelligence would not be possible without the massive growth in data available. The algorithms that exist today were there before too. What lacked was the data it needed to reach that next level. That level is here today.

Faster and More Affordable Computing Power

While data is important, it alone, without the computing power to process it, is nowhere near enough. The computers back in the day were not on the level that could potentially eat up everything put on its plate. Old computers were not even close to being ideal for training larger AI models. It would be an operation that would be too expensive even for the biggest companies in the world such as Google or Intel. With the introduction of modern GPUs everything changed. At first, the top graphics processors were used for games. From gaming to AI training was a leap across a pond, and we're not talking the Atlantic Ocean. Traditional processors were quickly replaced by GPUs where one was worth more than a dozen. The moment we've moved to cloud computing, things got even easier. With no need to buy, and ability to rent hardware, scaling up of AI burst onto the market. A barrier was removed and data started to free flow. It was possible even for smaller companies in the world of AI to train their models and make them competitive. With big players doing big things the research took a massive leap forward, and never looked back. Once computing was finally on the same page with previously available theories, it all started to make a lot more sense, and AI jumpstarted its adventure.

Model Design Breakthroughs

As we said, AI is not from yesterday. The reason why no one propped it up is because the early models were rather limited. They couldn't handle too many tasks, solving complex problems was an issue, and it limited its usage. New architecture aided in resolving the named issues. For one, the training methods got better, and when paired with more deeper and stable neural networks, magic was created. Yet, it all took another step forward when transformer models hit the scene. It was thanks to transformers that AI was finally able to understand context. It always understood the words, but early on it failed to recognize their relationships. Reasoning, code, and basic language were finally on the same level with transformers, and AI was able to tell the difference whether it is used as a writing model or to generate games for any modern [crypto casino](https://stake.com/de). Once it could fully understand every single word and its relationship to the rest of the sentence, AI became more quality. It was closer to humans than ever once it understood its texts, and once it was more like us, we started using it more and more.

Shared Knowledge Through Open Research

AI was not a secret kept hidden by any major conglomerate. The research revolving around it, moved forward fast because it was shared. Many researchers published their papers without licensing what they knew. Many public platforms keep AI code open even today, and many products were created by replicating others. A feedback loop created this way helped many researchers to make better end products by having access to someone else's failures. Unlocked progress was enabled by people with ideas being ready to share them. The massive pool of information created by universities, people doing it for hobby, and plenty of independent research, available to everyone was just part of the spark that ignited the AI revolution. Something similar is done by Stake.com and their Stake engine remote gaming server, available to all developers ready to give it a go at game developing. It is easier to build something when you don't have to build it from scratch and that's one of the principles on which the AI we know today was created.

Big Players Coming Onto the Scene

While AI research was done by both big and small players, it is no secret that even today it is quite expensive to deal with it on a serious level. Big models we use nowadays cost millions to develop. Even more. It was necessary for the biggest tech companies to make a leap of faith and to invest into the AI domain. Thankfully they did, as the biggest talent pool, money, and above all else infrastructure is tied to the biggest companies. In addition to what they had, companies such as Open AI, Google, Meta, and Microsoft, hired even more top researchers, built additional data centres, and invested heavily in projects intended to last. While AI started small, it is now handled solely by big name companies. The heavy competition we have now aids it to thrive even more. When one company makes a breakthrough, others are sure to respond, creating a competitive atmosphere that gives us a high quality AI product by the day. While top players are competing with each other on the same playground, they also diversify what they're doing and thanks to that we now have AI search engines, different writing tools, design software, and AI tools used for customer support. AI is no longer a dream theory, it is something people do use each day.

Better Training Techniques

No AI would be successful without proper training. Even the earliest of models were trained, yet, the results were not good enough. At times, the young artificial intelligence worked as intended. More frequently it gave strange results, and even worse, failed. Something needed to be done, and it was. Training methods improved vastly. It was all down to fine tuning by the researchers. The initial training was not able to hold water anymore. Work was done on refining the first training methods, and creating more advanced ones too. With time the usefulness and accuracy of AI was improved. Part of the process, of course, was the human feedback. Thanks to human input, AI models were able to understand what they've done wrong. The cooperation between humans and AI was a massive step in the rise of artificial intelligence. AI at its raw version is quite powerful but it provides messy results most of the time. Thanks to human feedback it became practical and aligned with human expectations. Another massive step was adding efficiency to the process, and when that moment came, the basic training wasted less data, and less computing power was necessary for the same amount of results as before. Better training made AI cheaper, easier to update, and more accessible to a broader audience.

Real World Demand

AI came to be not because it wanted to, but because we needed it to be. The number of businesses requiring automation found the solution in AI. Those who aimed at faster content creation got their answer too. Data analysis was elevated to a whole new level thanks to AI. All of these appliances desired for lower cost of operations and they've got what they've asked from artificial intelligence. Businesses that require fast and process customer support found an answer in AI, and the same goes for businesses that need faster generated reports, code written in time and reviewed, or creation of marketing content from scratch. AI is at their service. While many people feel endangered by AI, the fact is that the world of finances always was looking for faster solutions and the ability to scale one's business accordingly. Companies that saw the capabilities of AI and its direct appliance to their sphere of operations were quick to explore and invest into AI.

Everyday Integration

AI doesn't exist on its own. It is available through different tools. Mostly we have it in the tools we've already been using. That's what made it so appealing to regular people. AI can aid your writing and review every piece of your work in a matter of seconds. Image integration, smarter apps tied to emails, and AI summaries of everything on the web and beyond only one click away. In many cases, AI doesn't require an individual to learn a new system. It is only added to the software you've already been using. This is what matters the most to the regular Joe and his everyday business. Technology has been evolving each passing day without us even noticing, and these days AI is at the centre of most progress being made. It is only a matter of time when AI is going to become a natural part of our daily routines.

The Pressure of Global Competition

For many major players on the technological market, artificial intelligence has become a strategic priority. For big nations it is an economic advantage. For many companies it is a path to survival in the world of tomorrow. The competition in the world of AI is a global one, and the entire world is its playground. These days, more than a couple of governments are heavily funding AI research. You will find many universities expanding their programs to include AI research and studies into it, and major companies such as Google, Meta, Open AI, Tesla, or Samsung battling to attract top talent to their lap. The pressure made the existing timeliness run faster, and many deadlines have been met in advance. Yes, competition is messy at times, but there's no arguing that it is behind accelerated progress.

Acceptance Through Curiosity

AI was created by people for the people. Users were sceptical of AI, but also curious. The more time passed, the less choice the general public had. They've had to try AI. Once they did, the results came, the limits of what AI can do were pushed, and eventually acceptance came. It was a lot of work, but once it conquered social media platforms people started to become aware of AI and what it can do. In no time it got people engaging with it. Yes, the results were all over the place, but the engagement was there. Once the two started aligning, public interest rose further. With more people interested, more funding suddenly made sense, as there was profit to be made. Of course, the fears of losing jobs, privacy issues, and different ethical debates remained, but with each day the acceptance levels grew too. AI is now a part of every conversation on daily bases, at sports events, in pubs, in public transportation, and in corporate offices. It rises by the day, and there's no stopping AI now. For better or worse.