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.
End
免责声明：科技狗对文中陈述、观点判断保持中立，不对所包含内容的准确性、可靠性或完整性提供任何明示或暗示的保证。请读者仅作参考，并请自行承担全部责任。 本网站转载图片、文字之类版权申明，本网站无法鉴别所上传图片或文字的知识版权，如果侵犯，请及时通知我们，本网站将在第一时间及时删除：yzl_300@126.com
