Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
Central and Eastern Europe is well positioned to take a leading role in the development of AI in healthcare, but the creation of a marketplace for data is crucial. Just how important a role will artificial intelligence (AI) have in medicine over the coming years? That it will revolutionise healthcare is now beyond doubt, particularly in early diagnosis. Even so, its importance – and the need to speed up its implementation – cannot be overstated. Ligia Kornowska, the managing director of the Polish Hospital Federation, and a leader of the AI Coalition in Healthcare, is clear: "not to make use of AI," she says, "will soon be viewed as medical malpractice."
Asset-intensive organizations are pursuing digital transformation to attain operational excellence, improve KPIs, and solve concrete issues in the production and supporting process areas. AI-based prediction models are particularly useful tools that can be deployed in complex production environments. Compared to common analytical tools, prediction models can more easily amplify correlations between different parameters in complicated production environments that generate large volumes of structured or unstructured data. My regular talks with executives of production-intensive organizations indicate that AI use is steadily rising. This is in line with IDC's forecast that 70% of G2000 companies will use AI to develop guidance and insights for risk-based operational decision making by 2026.
The precision and promise of a data-driven society has stumbled these past years, serving up some disturbing--even damning--results: facial recognition software that can't recognize Black faces, human resource software that rejects women's job applications, talking computers that spit racist vitriol. "Those who don't learn history are doomed to repeat it," George Santayana said. But most artificial intelligence applications and data-driven tools learn history aplenty--they just don't avoid its pitfalls. Instead, though touted as a step toward the future, these systems generally learn the past in order to replicate it in the present, repeating historical failures with ruthless, and mindless, efficiency. As Joy Buolamwini says, when it comes to algorithmic decision-making, "data is destiny."
The amount of data being collected is drastically increasing day-by-day with lots of applications, tools, and online platforms booming in the present technological era. To handle and access this humongous data productively, it's necessary to develop valuable information extraction tools. One of the sub-areas that's demanding attention in the Information Extraction field is the fetching and accessing of data from tabular forms. To explain this in a subtle way, imagine you have lots of paperwork and documents where you would be using tables, and using the same, you would like to manipulate data. Conventionally, you can copy them manually (onto a paper) or load them into excel sheets. However, with table extraction, no sooner have you sent tables as pictures to the computer than it extracts all the information and stacks them into a neat document. This saves an ample of time and is less erroneous. As discussed in the previous section, tables are used frequently to represent data in a clean format. We can see them so often across several areas, from organizing our work by structuring data across tables to storing huge assets of companies.
An inordinate amount of some of the most vital aspects of Artificial Intelligence--from data engineering to data science, data preparation to machine learning--rely on one indispensable prerequisite: data modeling. Without effective data modeling, organizations can't integrate data across sources to build advanced analytics models. Data modeling is foundational to assembling training datasets, utilizing specific data for end user applications, and scaffolding predictive cognitive computing models. Consequently, it behooves companies to make the modeling process as efficient as possible to achieve the following three benefits that optimize their modeling endeavors--and the advanced analytics applications and use cases they support. These advantages are difficult, if not impossible, to realize with traditional relational approaches to data modeling.
Transmetrics' demand forecasting and predictive optimization platform is powered by artificial intelligence and machine learning algorithms. With four decades of experience and a strong operational presence in Egypt, KSA, UAE and Sudan, Transmar has built a solid reputation in the market, founded on family values that drive the company's ambition to offer the best in class service to its customers. Transmar owns and operates a large fleet of both dry and refrigerated containers, serves thousands of customers, and moves hundreds of commodities throughout the Middle East. "We strongly believe in the power of Data. Transmetrics' AI solution helps us leverage our 4 decades of operational experience, to make decisions both faster and smarter. As a regionally focused carrier we are more exposed to volatility. We're excited about the capabilities Transmetrics will provide by helping see up to 12 weeks into the future, ensuring we have optimum planning and repositioning plans" said Ahmed el Ahwal, Commercial Manager at Transmar.
Machine learning (ML) models are only as good as the data you feed them. That's true during training, but also once a model is put in production. In the real world, the data itself can change as new events occur and even small changes to how databases and APIs report and store data could have implications on how the models react. Since ML models will simply give you wrong predictions and not throw an error, it's imperative that businesses monitor their data pipelines for these systems. That's where tools like Aporia come in.
With more companies moving their business models online and adopting new solutions, it's been opening up more opportunities for cybercrime and identity theft. The spread of Covid-19 has only made it worse, as the Federal Trade Commission (FTC) estimated a loss of $13.4 million to Covid-19 scams as of April 15, 2020. This makes the protection of digital identity more important than ever before. This is also a new era of identity authentication. With artificial intelligence (AI) and biometrics, users today are enjoying more streamlined processes while reaping the benefits of added security.