After years of research, it appears that artificial intelligence (AI) is reaching a sort of tipping point, capturing the imaginations of everyone from students saving time on their essay writing to leaders at the world's largest tech companies. Excitement is building around the possibilities that AI tools unlock, but what exactly these tools are capable of and how they work is still not widely understood. We could write about this in detail, but given how advanced tools like ChatGPT have become, it only seems right to see what generative AI has to say about itself. Everything in the infographic above – from illustrations and icons to the text descriptions --was created using generative AI tools such as Midjourney. Everything that follows in this article was generated using ChatGPT based on specific prompts.

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Final week, Google tweeted about its interactive infographic on how Google Search has modified because it launched in 1998. For me, this infographic triggered a variety of search engine optimisation reminiscences. I tweeted about a few of them however the infographic might set off one thing else for you. Right here is the infographic within the how search works portal. Right here is Google's tweet – though Google deleted the unique one which I responded to: Rather a lot has modified since Google Search first launched in 1998.

Peak, a leader in Decision Intelligence (DI), released a new "State of AI, 2022" report highlighting that a number of organizations are making investments in both data and AI – without connecting the two. Surveying 775 decision makers (senior managers and above) from the US, UK or India, the report further reveals that when businesses are failing to prepare their data for future AI adoption, they risk needing to retrofit their infrastructure later down the road. Alarmingly, 97% of respondents stated that their business has or intends to implement a data strategy, yet only 35% of those say it includes plans to facilitate AI. Businesses are failing to realize they'll eventually need AI to remain future-proof, especially in this volatile environment.

Over the last few years Machine Learning became a very famous buzzword used in pretty much all cases and very often in situations where it shouldn't be used. Many comments are questioning whether it is a positive or negative technology but no matter what side ML sits it's coming. More and more companies are using ML and Ai in a way that was never used before and expectations all of us have are really big. To make ML closer to the people outside of the ecosystem I created this Infographic so that you can see how ML can actually be cool.

It's expected that 2019 will be the year, where investors move from IIoT (Industrial Internet of Things) proof of concepts to extending pilots for industry 4.0 solutions, namely digital twin, predictive quality, and predictive maintenance. IT and OT (Operational technology) have been raising for the past few years, and integrating is converging as the goal. Many of the investors are taking their IT and operational data a step forward to enhance the accuracy and relevancy of the data-driven insights. Contextualization is the next step. The only way for the investors to reach accurate conclusions, measure the right data, is by integrating all the relevant OT data from the line or plant environment with context data of the business from the IT systems.

While there are, especially in industries like manufacturing, legitimate fears that robots and artificial intelligence could cost people their jobs, a lot of workers in the United States prefer to look on the positive side, imagining which of the more laborious of their tasks could be taken off their hands by AI. According to a recent survey by Gartner, 70 percent of U.S. workers would like to utilize AI for their jobs to some degree. As our infographic shows, a fair chunk of respondents also named some tasks which they would be more than happy to give up completely. Data processing is at the top of the list with 36 percent, while an additional 50 percent would at least like AI to help them out in this. On the other side of the story, as reported by VentureBeat: "Among survey respondents who did not want to use AI at work, privacy and security concerns were cited as the top two reasons for declining AI."

From our humble beginnings as hunter gatherers, it took us thousands of years to create words with enough meaning to define the discrete schools of thought that make up primary sectors within society. Economics, law, medicine, philosophy and physics, to name a few. In the time since inventing these fields, we are in the middle of the next radical shift in the way we operate -- the main-streaming of interdisciplinary thinking and processes. In the past, innovations have been discrete and specific to an individual industry or application. For example, in the 20th century we invented electric power tools to replace manual tools.

To view the infographic full-size in high-res, download the pdf here. Data science is a powerful source of innovation and business value. As investments pour in and new technologies emerge, what are the most relevant and important trends to look out for? Best practices around tools, process and guiding principles for large-scale deployment are taking shape, allowing enterprises to solve Machine Learning at scale. Finally quality has got the edge on volume.