Artificial intelligence (AI) is currently a highly popular technology, with the industry expected to reach $22.6 billion by 2025. In the past year, AI-generated images and filters have become ubiquitous on social media, and tools like ChatGPT and DALL-E from Open AI have experienced explosive growth seemingly overnight.

Although AI shows promise, it still has a long way to go before companies and consumers can depend on it for crucial tasks. For instance, while ChatGPT can generate text, it lacks the reliability needed for accuracy. CNET, a consumer publication, tried using AI to write articles, but it had to issue extensive corrections to its AI-written content.

The occurrence of sloppy errors in AI, especially in critical fields like medicine, is unacceptable. Collaboration between AI leaders and experts from other technology sectors is imperative to establish fail-safe data validation methods and ensure the accuracy of AI-generated information, particularly in life-or-death situations.

The AI revolution

AI has the potential to disrupt the technological landscape and greatly benefit various industries, including Blockchain, fintech, and healthcare. If utilized with integrity, AI has the power to be as transformative as the internet was in the early 2000s. The limitations of AI are solely determined by the algorithms we develop, the data we input, and the computing power available.

From predicting cancer risk to preventing insurance underwriting burnout, AI will transform how we gather information and make critical decisions. This emerging technology can even remove racial and gender bias, making the outcomes from employee recruitment more fair and equitable.

AI-powered trading bots offer a way to level the playing field by eliminating human bias and emotion-driven decisions made by retail investors when trading stocks and cryptocurrencies. A data-driven approach can empower everyday investors to make smarter investment decisions and strategies.

The Dark Side of AI

AI technology has the potential to streamline processes and make our lives easier, but we must consider the serious drawbacks. Many people are concerned about AI and fear that it could lead to a dystopian future.

The internet has a long history of hoaxes, misinformation, and scams. The phrase Dont believe everything you read on the internet is deeply ingrained in our culture. Unfortunately, AI could exacerbate the spread of misinformation and negatively influence public opinion. Deep fakes, digitally altered videos that closely resemble real people, are a truly terrifying concept. Furthermore, an AI firm recently created a service that allows people to talk to deceased loved ones, which is deeply disturbing.

In addition to these eerie use cases, AI might not be conducive to productivity yet. A Stanford study found that programmers who use AI produce poor-quality code. If we are going to integrate AI into our workflows, it needs to be more efficient and accurate.

The potential of merging AI and blockchain

AI can sometimes make mistakes because of incorrect data. To prevent major errors, we should combine AI with other technologies like blockchain to make it more accurate. Blockchain is good for validating data because the data stored on its network is permanent and can't be changed by anyone. This ensures AI is working with correct information to give accurate results.

Putting data and decisions made by AI onto a blockchain database also makes it easy to audit and check if everything is right. Decentralized blockchain networks for AI could also prevent big tech companies from controlling all of AI, helping people trust it more.

AI may be able to use blockchain "oracles" that check information from multiple sources to verify it is accurate before using it. Some artists are already using both AI and NFTs, and it will be exciting to see what else these two technologies can do together. While AI could improve society greatly, it also has huge risks if not done properly. AI needs oversight, checks and accountability to prevent inaccurate or dystopian outcomes. We must be careful with this powerful but fragile technology.

Synthetic Data Over Real Data?

Artificial intelligence has taken over 2020, but aspiring technologists are facing a major roadblock: Training data. Building a large, well-curated dataset is crucial for most AI and machine learning applications, but obtaining this data is no easy feat. It's not just about collecting data from the real world; you also need to annotate and prepare it for your model.

For students, small research teams, and early-stage startups, training data is a significant hurdle to overcome. This is where Synthetic training data comes to the rescue. Synthetic data, which mimics real data, is invaluable for certain ML applications, as it's often easier to create than to collect and annotate real data. The fundamental law of machine learning is that you need a substantial amount of data. The quantity of data required can range from ten thousand examples to billions of data points. For complex applications like autonomous vehicles, gathering a massive amount of high-quality training data is a daunting task.

Fortunately, synthetic data is ideal for handling large datasets. Real training data is typically collected in a linear manner, where each additional training example takes roughly the same amount of time to collect as the previous one. This is not the case with synthetic data. What makes synthetic data unique is that it can be generated in enormous quantities. Need ten thousand training examples? No problem. A million examples? No problem. A billion? Well, as long as you have a more powerful GPU, it's doable. In comparison, collecting a billion real training examples might simply be impossible.