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AI news: 3D environment, metaverse, internet of things and blockchain

AI news: 3D environment, metaverse, internet of things and blockchain

The growing adoption of Big Data and predictive analytics is fueling the data analytics and AI market growth and is expected to continue. That is with what we come to the end of this turbulent year. Analysts advise that the global data analytics market is projected to grow up to USD 329.8 Billion by 2030 with CAGR of 30% from 2022 to 2030.

Market analysts’ estimates reveal that more than 181 zettabytes of data will be generated worldwide by 2025. And some of it will be added by OpenAI’s recent novice, ChatGPT. Revealed for public testing on November, 30th, this new AI tool reached 1 mln users within a week, thus becoming the most popular AI tool ever. Developers say ChatGPT generates text, audio and video in an unparalleled way. Its methods brought to life vivid discussions on whether AI in the form of ChatGPT or other tool will finally replace humans across diverse industries and areas of knowledge.

We are not sure AI will penetrate all aspects of our lives but it is the corporate life that AI will definitely affect. The survey on the state of AI from McKinsey's QuantumBlack demonstrates that business adoption of the technology has doubled in the last five years. 50% of respondents mentioned their business has adopted AI in at least one business area. QuantumBlack also found that AI is now being embedded into a wider range of business capabilities. The use of AI covers diverse applications, namely process automation, digital twins and facial recognition.

Large companies go along with this trend. One of the prime European banks, BNP Paribas Group took part in the “Banking and Auditability” working group, with the objective to develop a systematic and operational method for identifying the specific risks of AI systems, and remedial measures. The development and deployment of AI is one of the levers of the BNP Paribas Group 2025 strategic plan. More than 500 use cases are currently in production and the goal is to double the number of use cases and the associated value creation by 2025.

While banks set strategic goals in the area of AI development, some businesses use the technology for short-term profit. Take Disney, for example. Research team from Disney have revealed a new aging/de-aging tool that can make an actor look convincingly older or younger, without the need for weeks of complex and expensive visual effects work. Using neural networks and machine learning to age or de-age a person has already been tried. However, the results were not convincing and couldn’t be used on moving video. To cope with this, Disney’s researchers created a database of thousands of randomly generated synthetic faces. Existing ML aging tools were used to age and de-age these thousands of non-existent test subjects, and those results were then used to train a new neural network called FRAN (face re-aging network). Now real actors can be aged/de-aged professionally withing moments.

Another example of booming data market is the Chinese city and port of Shenzhen. In December it launched Big Data platform to serve the city’s cross-border e-commerce community. The platform is said to be the first trade data platform approved by China’s customs authority. It collects data shared by government agencies and connects to a network of trading firms, logistics service providers and financial institutions to create a cross-border trade service ecosystem.

What all businesses need is spreadsheets. You can’t imagine your work without it, can you? Google is now trying to change that paradigm for its Google Sheets online spreadsheet program with the help of ML. This month they announced a beta release of the Simple ML for Sheets add-on. Developed in the open-source TensorFlow project, Simple ML for Sheets will be available for users of all levels to leverage the power of technology while making simple calculations.

Now to our favourite topic – healthcare. So comforting to hear news from this area. In Australia the University of Queensland and Brisbane-based start-up Ariel Care have teamed up with a tech start-up to see how “care bots” can improve the lives of people who need high-level care. Their new technology responds to sounds, eye movements and gestures, and can flag seizures, fevers and falls. This technology has been used in gaming, and developers from Ariel Care re-created its new language with algorithms to let people use an eye movement or gesture to send a message to their carer’s mobile phone or computer. This contributes to creating a model of “smart high-care homes” that detect falls, fever, if someone has stopped eating, or wet the bed.

Another study investigated whether a quantitative in-silico score for coronary artery disease, based on a machine learning model, can be used as a clinical marker to detect coronary artery disease. The ML model used in this study was adapted from a previous model associated with short-term risk prediction of coronary artery disease through a binary framework. Importantly, analysis of data through ML opens a new avenue for evaluating a wider disease spectrum.

Weill Cornell Medicine announced a new study, stipulating that an artificial intelligence algorithm can determine non-invasively, with about 70 percent accuracy, if an in vitro fertilized embryo has a normal or abnormal number of chromosomes. Their new STORK-A algorithm uses microscope images of embryos taken at five days past fertilization, clinic staff's scoring of embryo quality, maternal age, and other data that is usually collected as part of the IVF process. Since the model uses AI, the algorithm automatically learns to correlate certain features of the data, often too subtle for the human eye, with the chance of failure.

Away from the planet Earth, ML is also used. Planetary scientists and astronomers discussed this month how new machine learning techniques have changed the way we learn about our solar system, from planning for future mission landings on Jupiter's icy moon Europa to identifying volcanoes on tiny Mercury. They demonstrated how ML can identify not only chunks of rock, but chunks of ice on Jupiter's icy moon Europa. Upcoming missions could also incorporate artificial intelligence as part of the team, using this technology to empower probes to make real-time responses to hazards and even land autonomously.

Back here, on the ground, ML helps to understand what we have in the oceans. New research from the Georgia Institute of Technology uses machine learning models to better understand water's phase changes. With their methods the researchers found strong computational evidence in support of water's liquid-liquid transition that can be applied to real-world systems that use water to operate. To better understand how water interacts, the researchers ran molecular simulations on supercomputers, which can be compare to a virtual microscope. The researchers analyzed how the molecules move and characterized the liquid structure at diverse water temperatures and pressures, mimicking the phase separation between the high and low-density liquids.

However, water is now always something to explore. In the southeastern Europe, water is often the cause of flooding, making thousands of people leaving their homes and property. A Croatian company has developed an integrated platform with real-time information: GDi Ensemble FloodSmart. This flood prevention tool collects all relevant information into a single web portal, which decision makers, government authorities and general public can access. It features data collection system, web portal, flood risk modelling, flood prevention and rescue model and is place in a secure cloud system.

To finish off with some optimistic thoughts, let’s take a look at Lloyd's Register Foundation World Risk Poll. The data from UK-based safety charity, which features responses from over 125,000 people in 121 countries, has revealed nearly two thirds (65%) of people living in Scandinavian countries believe AI will mostly help people in the future. We suggest we join this Nordic percentage and believe that the correct data and powerful technology will safely drive us through the festive season.

The discussion begins with the assertion that every company will eventually be forced to adopt AI. AI adoption varies across industries, with some seeing it as imperative for staying competitive, while for others, it's less of an immediate need.  New technologies like MLOps, data pipelines, autonomic databases, and predictive analytics help to improve the overall governance risk.

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April was full of new initiatives from vendors as well as some fascinating news on the technology front. With Olympics in Paris approaching, more  news will be flowing in from the capital of France.

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