The mainstream media presents AI as a monolith creation with a “one size fits all” lack of insight. This completely ignores that it is a complex system of algorithmic tools developed over decades. The field has seen rapid advancements in recent years, with new developments frequently making headlines. In January 2023, OpenAI made a major contribution to this trend by releasing ChatGPT, a free language AI with impressive accuracy and skill. Recent reports on AI are subject to a heavy recency bias. When it comes to AI, newer isn’t always better. An excellent example is Universal Logic’s Neocortex, an AI trained for robot movement and picking that combines deep learning techniques with earlier methods. Over the course of 15 years, Neocortex has completed over 34 million picks, giving it a robust ability to handle exceptions and adapt to changing circumstances. As you consider the implications of the state-of-art, let me regale you with the advantages of an AI database like the one for Neocortex.
Artificial Intelligence was coined in the 50s, and the original pioneers were dreamers who believed it could achieve rudimentary human intelligence. Progress was slow-moving at first, with most inventions ending up similar to Alan Turing’s WWII code-breaking machine. They were only really moving through 1s and 0s faster. For the longest time, AI was considered a pipe dream, most investors not believing it viable in the workplace. Even though the concept around AI platforms was conceptually fleshed out early on, with ideas such as neural nets created as far back as 1957. The field was stagnant almost all the way until the early 1990s. When the resurgence came, technology had finally achieved a sufficient enough level for AI to be sustained and grow. Solving two of its principal problems.
The number one problem with AI is scale, the simpler the AI, the easier it is to process information, but this information is equally as simple. AI development was handicapped by processing power up until recently. This limitation is because of neural nets and how they are set up. Neural networks are the framework for an AI to learn. At a high level, neural nets weigh decisions by assigning relative strengths to the inputs and hidden units to determine the output. But the actual computation within a neural network is quite complex. It involves mathematical operations such as matrix multiplications and activation functions, which must be performed repeatedly over many layers and numerous neurons. They are rows of nodes or neurons that will fire with some input present. Due to this processing requirement, early inventors could only create them with one or two rows. Still, deep neural networks can have millions or even billions of parameters. Which in turn requires massive amounts of computing power to function.
Each of these parameters needs to be trained. For an AI to deliver valuable feedback, it has to know what it is looking for. For each response, the AI has to be trained for correctness. For example, suppose you ask, “who is the president?” and it returns Joe Biden, Donald Trump, and Bill Clinton. In that case, we must distinguish which response is the most correct. Training takes time. This differentiates newer AIs with less experience from older ones, much like the difference between childhood and adolescence.
Now that computers have drastically increased their level of processing, the infrastructure for AI has skyrocketed. Neural nets have gone from a few layers deep to millions and millions of different layers. Because of this monumental rise, we witness traditional algorithms alongside AI, and the superiority is apparent.
AI databases have transformed how we store and access data, making traditional database management systems seem outdated and inefficient. Here, we’ll explore why AI databases are better than their alternatives and how they are revolutionizing the world of data management.
- Improved accuracy and efficiency
One of the critical advantages of AI databases is their ability to automatically and accurately analyze vast amounts of data. By using machine learning algorithms and statistical models, AI databases can quickly identify patterns and correlations in the data that would be difficult or impossible for humans to detect. This not only saves time and effort but also helps to ensure the accuracy of the results, reducing the risk of errors and bias in data analysis.
- Greater flexibility and scalability
Traditional databases are often rigid and demanding to scale, requiring significant effort to add or remove new data sources or update the structure of the database. On the other hand, AI databases are designed to be more flexible and scalable, allowing them to quickly adapt to changing data requirements and handle large volumes of data. This makes them an ideal choice for modern data-intensive applications that require real-time data processing and analysis.
- Better decision-making
AI databases can also help organizations make better decisions by providing insights and recommendations based on data analysis. By analyzing real-time data, AI databases can identify trends and patterns that might not be immediately apparent, enabling businesses to make more informed decisions about their operations, products, and services. This can improve efficiency, customer satisfaction, and business outcomes.
As an industry set to see massive expansion over the coming years, AI will soon dominate the data landscape. As we look toward the future of AI and its impact, it’s important to remember the slow and steady progress that brought us here. From the early dreamers of AI in the 50s to the recent release of ChatGPT, the road to advanced AI has been long and winding. But we are on the cusp of a revolution with improved accuracy and efficiency, greater flexibility and scalability, and better decision-making that AI databases provide. Join us in being a part of this new world. With Neocortex as the next step, you can get ahead of the industry with the longest-running 3D vision AI in the world. Embrace the power of AI and look forward to the exciting possibilities that lie ahead. Who knows what extraordinary innovations will come next?
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