AI and machine learning are best-suited to specific types of tasks and these vary in complexity. For this reason, it is important to determine what you’re looking to accomplish so that you can intelligently configure and aggregate your data for the AI process to produce favorable results.
Most people have heard of blockchain in conjunction with cryptocurrencies such as Bitcoin or smart contracts like Ethereum. While these applications demonstrate blockchain’s disruptive power, its decentralized, trustless technology has the power to do so much more for businesses and brands everywhere .
Imagine you have an exciting new product. It’s something your team has spent months brainstorming, planning, tweaking, and honing until it matches your collective vision. You did everything right internally. But then, after spending all that valuable time, money, and resources, something doesn’t work as you thought when you release it to the world. Something you didn’t foresee stands between your product and a happy end user. Now you’re forced to start over from scratch.
AI is not a replacement for human thought. It’s a symbiotic partner. Successful applications of AI require more than just big data, powerful processing, and complicated algorithms. Designing truly useful AI requires a complete understanding of user needs, experiences, and, on an even deeper level, psychology.
Understanding the five stages of Design Thinking is the first step to putting this innovative methodology into practice. When put into practice, Liquid’s Design Thinking for AI optimizes business data and functions with a focus on understanding the human-centered experiences that both created the data and will be driven by the data DTAI develops intelligent solutions so you disrupt your industry before your competitors.
Testing is the most critical step of any successful machine learning project. It demonstrates whether your algorithms, weights, biases, and labels are correct or need to be improved. Read our white paper to learn the 10 critical steps to ensure successful testing of your machine learning application.
Garbage in, garbage out. I’m sure you’ve heard the phrase before. It can apply to relationships, dieting, working out, job performance, you name it: in order to get the best results, you have to fully commit to the best practices. Sure, it may sound simplistic, but it’s also true for machine learning projects. The quality of your model’s predictive output will only be as good as the quality and focus of the data it receives.
You have likely felt the buzz in the business community about artificial intelligence (AI) – how it is transforming every business process from sales and marketing to customer service and throughout the entire supply chain. But despite all the talk, only one in five executives have deployed an AI solution to support core aspects of their business. The best place to start is find the right partner who has the experience, team and confidence to overcome the barriers.
Design thinking on the business side is used as a guide to problem solving so that we can get out of our ruts and develop innovative and often out-of-the-box solutions. In a world where we have somehow managed to glamorize failure into a celebrated roadblock to success, Design Thinking allows you circumnavigate failure so that you can instead celebrate a prudent achievement.