Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. If you have been connected with the IT news over the last month, you have undoubtedly heard about ChatGPT -- the new AI chatbot from OpenAI. As Andrew Ng rightly said, AI is the new electricity. It is set to revolutionize each aspect of our life, and ChatGPT will change the entire Software Development Life Cycle.

With the recent rise in popularity of text-to-image image generation engines, our friend Pratik Naik had a chat with one of the most popular A.I. chatbots, Open A.I. In his words, the conversation title is "I asked A.I. if humans have anything to fear when it comes to text to image A.I. generators? Here is what it said." It was an interesting conversation, although with a somber conclusion. We are bringing this interview as is and would love to hear if you are concerned about AI or consider it as an opportunity.

As businesses look for ways to transform their organizations in an era of uncertainty and emerging competitive threats, many are turning to artificial intelligence technology. Hundreds, if not thousands, of software companies are already designing their products with a foundation of AI. AI can provide significant tools to help businesses take advantage of their data and knowledge. But as with any complex business transformation, there are no quick solutions – solving complicated problems requires careful planning and a well-constructed roadmap. What is the best way to embark on an AI strategy that can help support your business goals? Three key imperatives can help your organization make good use of this powerful capability.

The 1980s saw the evolution of computing as it transitioned from industry to homes through the boom of personal computers. In the field of data management, the Relational Database industry was developing rapidly (Oracle, Sybase, IBM, among others). Object-oriented abstractions were developed as a new form of representational independence. The Internet changed the way people communicated and exchanged information.

While the biomedical community has published several "open data" sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 publicly available biomedical linked data graphs into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.

It's true, the recent advancements of narrow AI are mind-blowing: algorithms are beating humans in applications ranging from gaming to healthcare. But however'magical' these accomplishments may seem – especially when we retrospectively look back at what we thought AI would be able to do just a couple of years ago – this is far from the reality of the everyday work we do at Ericsson. For us, the'magic' of AI is making it work for us to make our everyday lives better and more efficient as a result. Now is the moment when AI goes from hype to reality. Already in our own industry we can see that AI is being embraced by service providers around the world.

LDK brings together researchers from across disciplines concerned with the acquisition, curation and use of language data in the context of data science and knowledge-based applications. With the advent of the web and digital technologies, an ever-increasing amount of language data is now available across application areas and industry sectors, including social media, digital archives, company records and so-on. The efficient and meaningful exploitation of this data in scientific and commercial innovation is at the core of data science research, employing Natural Language Processing (NLP) and machine learning methods as well as semantic technologies based on knowledge graphs.

But understanding and interpreting language as humans use it is becoming a pivotal part of both machine learning and artificial intelligence.