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Forget Blockchain and Invest in AI

Since the dawn of computer systems, there have been technologies competing for everyone’s attention — the past few years have been no exception. Augmented reality, the Internet of things (IoT), blockchain, and artificial intelligence are the technologies capturing the interest of most technophiles today. The last two, blockchain and AI, have become major talking points for business applications. According to a survey of innovation leaders from 20 of Atlanta’s top companies, these are the top 2 technologies most companies want to learn more about. If you follow sites like Product Hunt, you will see a new product powered by one (or both) of these technologies launches nearly every day.  So which one should you pay attention to? Which is more likely to affect your company? This article seeks to answer those questions and more.

First up is Blockchain!

At its heart, blockchain is a secure and distributed ledger. Picture a spreadsheet that is duplicated across a large number of computers. Then imagine this network of computers regularly updates this spreadsheet simultaneously. This means the records are public and verifiable. Because there’s no central location or singular point of failure, blockchains are generally much harder to hack as data exists simultaneously on every computer in the network.

So why is this valuable? If you look at the world today, we rely heavily on third parties to verify the authenticity of something. Take banks for example. We depend on banks to verify the availability of funds or if a financial transaction is valid. A single entity or group of entities typically owns and manages these services. So what happens if someone hacks their systems and alters your financial history? With the growing number of data breaches in the headlines, the fact that we rely on these centralized authorities for so many critical needs leaves us more exposed than ever.

From land administration to voting to supply chain auditing, blockchain can provide the much desired transparency that people often feel is missing. This has huge implications for who we currently entrust to handle these important transactions. We will no longer need these “middleman” services. So while it’s easy to see blockchain will have a profound impact on the industries and services you depend on every day, it is a little more difficult to see its value when it comes to most business needs.

Now I’m sure if you try hard enough you could find a use for blockchain within any business. There are a number of services centered around blockchain that would love to lead you to believe that. But the problem I see is most businesses are not going to be more competitive, more efficient, or more profitable simply because they leverage blockchain technology. This is why I believe most companies shouldn’t waste their time and resources looking for ways to add blockchain to their list of services or technology stack.

The Importance of AI

“Blockchain tries to keep man honest while AI tries to replace man all together.”

What is AI and how is it different from deep learning or machine learning? Let’s take a look at how Google defines AI:

“Artificial Intelligence is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”

Basically, AI is the broader concept of machines being able to carry out tasks in a way we would consider “smart”. Computer vision, natural language processing, machine learning, and deep learning are just different sub-disciplines of AI. Some of these disciplines try to solve different problems, while others try to solve the same problems in different ways. While I don’t think it’s valuable for me to list and explain all of them for you, I do think it is important to briefly explain the ones that you are most likely to hear about.

  • Computer Vision – teaching computers to see much like humans do. This technology is typically used for object detection (ex. fruits, vehicles, home goods, etc) and facial recognition. Many of today’s autonomous vehicles rely heavily on computer vision.
  • Natural Language Processing (NLP) – teaching computers how to understand human language as it is spoken. This technology is typically used for sentiment analysis, understanding the intent of a message, spam detection, etc. To put it in simpler terms, this technology is used to understand the meaning and context of what a person is saying
  • Machine Learning – developing computer programs that can access data and use it to learn for themselves. The idea is that the more quality data (the key here being quality) you are able to provide, the better the algorithm will get at understanding what to do. This technology can be used for things like product recommendations, understanding customer churn, forecasting, and prediction. Machine learning is one of the most popular and highly used forms of AI.
  • Deep Learning – This is a subset of Machine Learning that leverages neural networks to analyze data using a logic structure that is similar to humans. Essentially, it aims to process information like the human brain does. Currently, it is the best performing form of machine learning and why you may see these two terms used interchangeably.

Why Should I Invest in AI?

Today, the average cell phone has more processing power than the entire world did back in 1956, when the field of AI was first started. This advancement in hardware makes processing the vast amounts of data required for most AI technologies possible. While I believe anything near human-like intelligence is a ways off, you don’t need human-like intelligence to get value from AI.

Simpler things like predicting future sales in a new market or understanding user segments can provide more than enough value for your business. Many large enterprises already take advantage of AI in some form to gain valuable insights into their customers and business.

But what about smaller businesses? For many smaller and midsize companies these technologies have been traditionally too difficult and costly to implement. Luckily, many of the recent advancements in AI technology have not just been enhancing performance, but making these tools less complicated and more accessible.

There is now tooling that can allow developers and analysts (who do not have a PhD) to take advantage of AI too. And thanks to the explosive growth and relatively low cost of cloud computing, there are countless services that can help your business immediately gain insights from your data or provide other AI technologies that you can easily integrate with.

What are some of the things we can do with AI today?

  • Understand what people are saying
  • Understand how people are feeling
  • Understand the relevance and context of information
  • Identify what is in/happening in pictures or videos
  • Make forecasts and future predictions
  • Detect anomalies (ex. fraudulent transactions, cybersecurity)
  • Classify people/things into distinct groups
  • Find correlations in data

While not all of these use cases are going to be valuable to every company, forecasting, user segmentation, and understanding what people are saying about your company or products are needs universal to almost every business – makingAI technology a much better investment of time and resources compared to blockchain.

The key to a lot of it (besides getting the right data) is understanding how things like detecting anomalies, segmenting users or making predictions can be applied to your organization.

Specific use cases always help to solidify things in our minds; here are some of our favorites we hope will resonate with you and make it clear how AI can apply to your respective worlds.

Keep in mind these are just the tip of the iceberg, but hopefully can help you find ways to get value from AI in your organization.

Want to learn more about AI or speak with companies already leveraging AI? Come to one of our upcoming Product and Innovation Community events – a monthly speaker series focused on connecting intrapreneurs to the insights they need for success.

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