The world is facing a global AI talent shortage, so while there’s a great demand for NLP implementations, the supply of data scientists needed to bring these projects to life are limited. But what if we could democratize NLP, reducing the need for data scientist intervention?
When it comes to the more creative tasks, natural language processing (NLP) lacks one thing: the human touch. However, this could soon change thanks to one simple technique we as humans use to learn language: listening. Google is now training its NLP algorithms with human dialogue.
We’ve all had our fair share of experiences with a bad chatbot, but chatbots are getting smarter and research shows that they will reach a 90% customer interaction success rate by 2022. Here are some key chatbot mistakes you don’t want to make and how to avoid common pitfalls.
Dan Turchin, CEO of PeopleReign, talked with us about the benefits of AI-based virtual agent systems in the enterprise. How are they being used by organizations today? What are the practical benefits and the broader impact of virtual agent technology on employees and companies?
Some believe that artificial general intelligence (AGI) will wipe out the human race, while others are confident it will lead to a utopian society. Between these extremes, most people anticipate major changes as AGI becomes more prevalent. What are the risks and dangers that will emerge?
To be the next-generation voice assistant, you need to be a concierge that truly understands your user’s preferences, learns them, and keeps track of context for personalized results. How do you keep track of preferences and other relevant activities such as recent searches?
Chatbots do more than just messaging. They are rapidly adding value to conversations and have context-driven intelligence that aims to solve customer problems in a convenient matter. This is just the tip of the iceberg. In this article, we will talk about the must-haves in your chatbot testing checklist.
How do you create Artificial General Intelligence (AGI)? No one precisely knows. If we wait for a complete understanding with robust mathematical models of AGI, we may never get started. Given that, a more experimental and iterative development approach is in order.
Overall, Java developers love Spring/Spring Boot because it saves them time and supports their testing experiences. The Diffblue Survey found that Spring’s standardized testing approach makes it easier to apply a technique from artificial intelligence (AI) called Reinforcement Learning to automate test-writing. Making this work for Java developers can slash development time as well as improve code coverage.
Software design is also an area where AI can prove to be very effective. There are various benefits of AI-powered coding. AI-assisted tools can help developers to reduce around 50% of the number of keys pressed in the development of software.
The rapid growth of AI also means that technology is making more and more decisions without us questioning them. Whether it is loan approvals, job recruiting or facial recognition, companies now more than ever need to make sure their AI applications do not discriminate against humans. To minimize the risk of AI bias – the unintended distortion of decisions by artificial intelligence – developers must keep a number of things in mind.
Why does DIY AIOps fail and what is the root cause? In many cases, all the time and effort put into a do-it-yourself project simply winds up being wasted. This article looks at how to safely encourage AIOps exploration and measure ROI from AIOps without the risk of failure.
Justin Reock, Chief Architect for OpenLogic at Perforce Software, describes what is software fuzzing and why it is needed. Justin is the author on a chapter about fuzzing in a new book from Perforce Software: “Accelerating Software Quality: Machine Learning & Artificial Intelligence in the Age of DevOps”.
AI makes it possible to process and interpret huge quantities of data far more quickly and efficiently, potentially unlocking a whole host of new insights. In this article, we’ll take a look at what AI is doing for data centers and why you need to make the most effective possible use of this transformative technology.