Where Are AI and Machine Learning Today?
Where Are AI and Machine Learning Today?
This snapshot of AI and machine learning covers strategy tips, real-world problems, pending opportunities, and what devs need to know to take advantage of them.
Join the DZone community and get the full member experience.Join For Free
Thanks to Matt Coatney, V.P. Services at Exaptive for taking the time to talk with me about the state of AI and machine learning today and how he sees it evolving.
Q: What are the keys to a successful AI/machine learning strategy?
A: Not unlike the DevOps movement, it has more to do with the people and the approach, since the new technology is introducing a change in business management strategy. On the one hand, it can replace tasks that people have been doing and do those tasks more effectively, reliably, and efficiently. On the other hand, new business models are feasible where they weren’t before.
A couple of examples Matt shared:
- In medicine, IBM’s Watson detected a completely different strain of leukemia than the group of doctors had even considered in less than 10 minutes.
- Atomwise, a Silicon Valley biotech, is looking for existing drugs to apply to new targets and found two drugs that prevented the spread of Ebola in one day. This type of research used to take years.
Q: How can companies get more out of big data with AI and machine learning?
A: Companies spend too much time on the technology they think they need versus focusing on the technology needed to solve a particular business problem. Companies need to think about the problem they are trying to solve and how to make the solution palatable to the consumer. Think about how to make the solution effective so you can realize a positive ROI and move on to the next project or opportunity. Define your success metrics and get quick wins. It’s not that different than the projects we’ve been doing in IT for the past 20 years, we just need to keep the best practices in mind.
Q: How has AI/machine learning changed in the past year?
A: A lot of approaches have been the same for the last 50 to 60 years, it's just that we have far more powerful computers with more memory and optimized algorithms like deep learning, so that we can now get better results in a fraction of the time. Examples include Facebook’s facial recognition and Google’s self-driving cars. In addition, we now have AI as a service where companies can rent time from a computer, issue requests, and get information back in record time. This lowers the barriers to entry while ensuring any organization gets the same level of quality as the Facebooks and Googles of the world.
Q: What are the technical solutions you use to collect and analyze data?
A: Most companies focus on the big data “Hadoopesque” tools. We can do that, but we also find value in smaller data using tools like SQL, NoSQL, Oracle, Microsoft, and Python's scikit-learn library to get novel results without investing millions. There is still a lot of value to be mined from existing data regardless of size.
Q: What real-world problems are your client solving with AI/machine learning?
Anything around forecasting, reconnecting, or predicting content — Netflix-style applications. Financial modeling and the democratization of advanced financial models. Also, content and knowledge management tools that help organizations get more insight and value from their content by tagging concepts, keywords, etc.
Q: What are the most common issues you see preventing companies from realizing the benefits of AI/machine learning?
Companies are focusing on tools and platforms instead of the business problem they are trying to solve. They need to separate the hype from reality, understanding what tools can and cannot do. The marketing hype is being bought and creating unrealistic expectations. There needs to be better vetting and understanding of the tools. Understand that it takes time to train AI for the industry and use case (e.g., how lawyers write and talk).
Q: Where do you see the biggest opportunities in the continued evolution of AI/machine learning?
- A: I’m excited about AI as a service and the opportunity that provides for developers and entrepreneurs looking to start a business quickly without a lot of expense.
- Decision support and automation in the knowledge space. Greater perspective on problems leads to better, less biased, solutions.
- The merge of the physical and virtual world with robotics.
- Use data to solve business problems. Google’s data centers use 25% of a nuclear power plant every day. Google used Deep Mind to optimize all their servers and reduced energy consumption by 15 to 20%. Ultimately every business will be able to realize the same type of OPEX savings.
Q: What are your biggest concerns regarding the state of AI/machine learning today?
A: Will AI be used for good or evil? It is neutral. It depends on how it’s applied and who applies it. We need international oversight. It is already being used in cyberwarfare.
Avoid getting stuck in a local maximum. We’ve used the same hardware and software architecture for the last 60 to 70 years to do something infinitely more complex than we’ve ever done before. We need to explore different approaches to exponentially improve performance.
Q: What skills do developers need to work on AI/machine learning projects?
A: Start with soft skills. The best developers and data scientists have paid attention to improving their project management, communication, and time management skills. Focus on understanding abstract concepts and be as well-rounded as you can with different languages and technologies. Embrace creative destruction since the landscape is fluid and changing rapidly.
Q: What have I failed to ask that you think developers need to know about AI and machine learning?
There is a lot of misconception around terminology. We need to get clarity about what we mean when we use these terms:
- Machine learning is how we use software to learn something.
- AI is synonymous with machine learning but tends to connote a more advanced, human level of capability.
- Deep learning is a specific machine learning technique that is capable of handling more nuanced learning, which tends to be associated with AI.
Opinions expressed by DZone contributors are their own.