ChatGPT, an AI model of Muse, an Earth AI on the moon
The start of the year is rather active with all that news around ChatGPT and its ‘cans and can nots’. We tried to avoid this topic and to look for more modest news. Since there is a lot happening the market of AI and data analytics without chatbots.
However we will take a short look at the news from big guys who are focused…, well, on ChatGPT.
Microsoft and OpenAI, the hottest AI lab in the world, announced that Microsoft would be investing $10 billion into OpenAI. It has to be mentioned that Microsoft already gave OpenAI $3 billion since 2019. But with the top-notch ChatGPT OpenAI is a much-wanted asset. Microsoft is rumored to be adding ChatGPT to its Bing search engine. That means this little-used Bing might finally become a real competitor to Google’s search.
Meanwhile Google AI released a research paper about Muse, a new Text-To-Image Generation that can produce photos of a high quality comparable to those produced by models like the DALL-E 2 and Imagen at a rate that is far faster. Muse uses a 900 million parameter model called a masked generative transformer to create visuals instead of pixel-space diffusion or autoregressive models. Google AI has trained a series of Muse models with varying sizes, ranging from 632 million to 3 billion parameters, finding that conditioning on a pre-trained large language model is crucial for generating photorealistic, high-quality images.
While tech giants are making such advances in their technologies, let’s take a more down-to-earth approach and see what’s going on at the beginning of the year in healthcare. Trinity Health and Strive Health announced plans to enhance treatment for chronic kidney disease and end-stage kidney disease for Loyola Physician Partners patients using predictive analytics and machine learning. One of the most prominent nonprofit Catholic healthcare systems in the US, Trinity Health includes 123,000 employees and almost 27,000 physicians and clinicians. Their new partner Strive Health supports value-based kidney care using a combination of care teams, predictive analytics, advanced technology, and integration with local providers.
In the world of sports, news come from various sources. The world of Formula One has long become a world driven by innovation and data. Formula One cars have 300 sensors producing 100,000 data points, accumulating 1.5 terabytes of data over the course of a race—all of which are analyzed to inform everything from car design to racing strategy. By measuring track temperature, tire degradation, aerodynamics, and a vast array of other performance indicators in real time, a racing team can use this data to make adjusts on the fly. With the help of data analytics firms like Alteryx (which works with McLaren Racing, for example) no knowledge of or experience in computer coding or designing algorithms is needed to treat tools and develop dashboards that leverage the power of predictive modeling.
Inspiring case from the basketball. In Wizards team there is a special position for a visionary in basketball analytics. These experts explain players how they can change several areas of his game through a data-driven approach. One example can be their free throws.
For those who prefer doing sports on a couch. Sports betting is an area of the gambling industry that has seen a massive surge in popularity in recent years. AI makes it even more attractive. With the help of AI-powered predictive analytics, sports bettors can take their bets to a whole new level. AI is having a major impact on the sportsbook industry, as it has been used to create more accurate and reliable odds. Its algorithms can analyze vast amounts of data in order to accurately predict the outcome of sporting events. This allows bookmakers to set more precise odds that reflect the true probability of an event occurring.
Out there, on the Moon, we will soon see our AI. A Canadian machine learning system will make its way to the moon's surface onboard a United Arab Emirates rover that launched with SpaceX last December. UAE’s rover is expected to run for approximately one lunar day (29 Earth days) on the surface. And AI will assist in collecting and analyzing the data. It is great to see such examples, since no AI has ever reached beyond low Earth orbit before.
In the nature world yet another step was made to track the health of coral reefs by analyzing the sounds and noises emitted by them. Exeter University in the UK uses a new AI-based technology to monitor the progress of coral restoration. Analysing their special songs through AI allows researchers to obtain data useful in measuring the health of corals and launch restoration projects when necessary. The series of recordings successfully determined the health status of the reef 92 per cent of the time.
Another case from the world of nature is by ESP, a non-profit organization from California. They have developed a machine learning system that decodes animal communication by identifying patterns in behavioral ecology research.
This includes analyzing large data sets that contain visual, oral and physical animal communications. The goal, the researchers say, is to determine under what conditions an animal produces a communication signal, how the receiving animal reacts and which signals are relevant to influencing actions. The system uses 10 datasets of various animal communications and establishes a baseline for ML classification and detection performance. The datasets being studied in various efforts to decode animal communication include recordings from a range of species like birds, amphibians, primates, elephants and insects like honeybees. Communication from domesticated cats and dogs is being studied, too. Yet experts note that communication among cetaceans—whales, dolphins and other marine mammals—is especially promising.
A combination of predictive analytics and deep learning helps improve poultry management and provide traceability throughout the entire supply chain. A software company AbuErdan developed a poultry management software, which collects and analyzes data on trends, key performance indicators and performance in poultry production. Understanding the relevant KPIs allows businesses to take accurate action in the 'breeder' stage and consequently increase production capacity. Poultry managers can check KPIs from the hatchery on their dashboard and link the egg production date to the flock's production week. The available data allows managers to take the right actions related to the mating ratio and other factors that have a great impact on the production rate.
UK-based utility giant EDF presented a new case of using data assets. The organization wanted to find a way to exploit its treasure troves of data and create pioneering services for its customers using up-to-date data analytics and machine learning technologies. In the long-term process of digital transformation EDF moved from a disparate collection of bespoke and off-the-shelf systems to a tight enterprise data strategy based on the tactical use of cloud-based services.
TMX Group Ltd, owner of the Toronto Stock Exchange, invested $175 million into a U.S. data analytics company VettaFi Holdings LLC as it looks to expand its information services unit. As part of the deal with New York City-based VettaFi, which provides a database of exchange-traded funds, analytics and indices, the exchange operator's analytics business, TMX Datalinx, will offer index and ETF services. Exchange operators globally have been trying to diversify their business models and bring in additional sources of revenue which are less sensitive to rapid shifts in market conditions. In 2020, New York Stock Exchange-operator Intercontinental Exchange Inc agreed to buy mortgage software firm Ellie Mae. The same year, Nasdaq Inc also acquired fraud detection firm Verafin.
Well, those money boxes know where to invest. Perhaps, we should follow their path and see where it takes us this month.