Neural network (NN) models are the brains that comprise AI algorithms. These models are inspired by how a human brain processes information to identify things. While the growing number of available models is staggering, there are several standard models which are used as a starting point when designing more complex models.
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.
As more businesses are incorporating AI and ML into their strategy to reduce costs and create better products, they are gaining a competitive advantage over those holding on to traditional means of operation. Employed effectively, AI and ML will provide tide-turning returns for companies. If you’re looking to introduce AI into your business strategy, how do you choose the most effective business cases? How do you measure the empirical value?
As AI is increasingly adopted globally, it is vital to consider how AI can bolster your business. Gartner predicts that by 2020, 85% of customer interactions will be managed without a human. While AI is no silver bullet, when applied effectively, it can enable businesses to tune their customer interaction, marketing, and product design to competitive levels.
The most disruptive force in marketing today isn’t what you think. It’s not a machine. It’s not a device. It’s not an AI interface or a machine learning model or a conversational assistant. It’s the consumer. More specifically, it’s what consumers have come to expect from their brands. And if you can’t meet the needs and preferences of the next-generation consumer, your brand won’t stay relevant for long.
The surest way to grow your brand’s market share is to engage with consumers in a personal and meaningful way. By personalizing UX on every level, businesses give themselves the best chance at scaling their growth. But how do they do this in the realm of eCommerce, where there is often no human-to-human interaction?
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 .
AI and blockchain are two of the most disruptive technologies in the world today. Whether it’s using machine learning to narrow down a customer’s preferences, or utilizing blockchain to create a secure, robust database, it’s hard to get very far in the modern marketplace without them.
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.
Artificial Intelligence (AI) is one of today’s most exciting and versatile business tools. As Google’s CEO, Sundar Pichai says, “AI is more important than fire and electricity.” One of the most useful ways it can enhance our daily workflows is by removing the need to repeat processes or struggle with tedious endeavors when our time could be better spent on higher-value tasks. We are seeing more AI infiltration every single day.
Today, things tend to be a little different. Now we get major iOS revisions every year and with the introduction of Swift back in 2014, the annual upgrade has become complicated. To be clear, I’m actually okay with all of this happening, as it’s actually very good to see so much development effort being put into Swift, but as the person doing the upgrade, the details can be highly frustrating.
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.
Summarizing the key learnings from Northwestern’s Kellogg School of Management’s Design Thinking workshop and pulling together various resources as well as articles on the subject. After graduating with an MBA from Kellogg in 2005, I have been involved in many aspects of user experience and this workshop brought it all together for me.
Design Thinking consists of applying the (design) process of designing physical products to business decisions, services and even process improvement initiatives.
When Amazon Echo with Alexa service came out in November 2014 I was skeptical. A speaker with voice recognition seemed like an unnecessary oddity.
Alexa’s SDK has been open to third party developers for a year now. As a software engineer it is important to keep up with emerging technologies and learn about them. I purchased an Amazon Echo about a month ago and had an opportunity to interact with the technology and try out the SDK. Subsequently, I discovered these important truths:
Having grown up as Generation Y and watching technology grow at what seems like hyper speed, I can’t describe just how interesting it has been watching each generation interact with new technology, new devices and how much the latest generation (Millennials) has influenced the rapid request for change across many industries.
The usefulness of NLP is largely seen when combined with machine learning capabilities. With regards to being leveraged by businesses, when a machine can learn the semantics and context of the questions it is asked and statements it is given, it will enable businesses to unlock the potential of its large volumes of data to streamline processes and extract information without needing the help of a subject matter expert.
Machine Learning is creeping into companies of all sizes and I’ve found that many of those who want to implement it are those who aren’t in IT. Business clients are able to explain the results of machine learning within an application or set of applications, yet have trouble understanding exactly what machine learning actually is, how it works and why it takes longer than they think to get the results they want.
In previous tutorials, we went through CRUD with LPKTutorialOne, and Function IDs / QueryFilters / Composites with LPKTutorialTwo. In this tutorial, I want to go over common components that LPK offers, that will expedite the process of building your iOS application.
Note: The components covered in this tutorial offered by LPK are only supported for the iPaddevice.