In a widely circulated and discussed article on Forbes, Nallan Sriram, Global Technology Strategist of Unilever makes a compelling argument for the need for master data for AI initiatives in the enterprise. The article describes that master data gets siloed in operational systems like ERP with the key decision-makers realizing the need for correct master data when faced with revenue loss or increased operational expense. As master data provides context to business transactions, it is fundamental to business operations. In earlier times, we could manage master data through human intervention. But now with cloud data lakes and our aspirations to build predictive algorithms for business operations and operations, the need for clean, contextual and unified master data is all the more enhanced.
The BDN Opinion section operates independently and does not set newsroom policies or contribute to reporting or editing articles elsewhere in the newspaper or on bangordailynews.com. Keith E. Sonderling is a commissioner on the U.S. Equal Employment Opportunity Commission.The views here are the author's own and should not be attributed to the EEOC or any other member of the commission. With 86 percent of major U.S. corporations predicting that artificial intelligence will become a "mainstream technology" at their company this year, management-by-algorithm is no longer the stuff of science fiction. AI has already transformed the way workers are recruited, hired, trained, evaluated and even fired. One recent study found that 83 percent of human resources leaders rely in some form on technology in employment decision-making.
The United Kingdom has ruled out that AIs or artificial intelligence cannot set forth its patents or have an invention or innovation named after them after an appeal by one Dr. Stephen Thaler. The researcher has set forth several innovations to be named after the AI he used, something which is somehow unnatural for the courts. The case of Thaler vs. Comptroller General of Patents Trade Marks and Designs has focused on the United Kingdom's view on AI and its right to push forward an invention for society. It has examined the possibility of having an AI take credit for its work, something which was not yet that of an open possibility in the country. That being said, the "AI DABUS" or technology by Thaler, which he put down as the "inventor" of the technology that they are trying to patent, was dismissed and denied.
Regulatory bodies around the world increasingly recognize that they need to regulate how governments use machine learning algorithms when making high-stakes decisions. This is a welcome development, but current approaches fall short. As regulators develop policies, they must consider how human decisionmakers interact with algorithms. If they do not, regulations will provide a false sense of security in governments adopting algorithms. In recent years, researchers and journalists have exposed how algorithmic systems used by courts, police, education departments, welfare agencies and other government bodies are rife with errors and biases.
Add the United Kingdom to the list of countries that says an artificial intelligence can't be legally credited as an inventor. Per the BBC, the UK Court of Appeal recently ruled against Dr. Stephen Thaler in a case involving the country's Intellectual Property Office. In 2018, Thaler filed two patent applications in which he didn't list himself as the creator of the inventions mentioned in the documents. Instead, he put down his AI DABUS and said the patent should go to him "by ownership of the creativity machine." The Intellectual Property Office told Thaler he had to list a real person on the application.
Launch a National AI Research and Innovation Programme to improve coordination and collaboration between the country's researchers, while "boosting business and public sector adoption of AI technologies and their ability to take them to market." Launch a joint Office for AI (OAI) and UK Research & Innovation (UKRI) programme aimed at continuing to develop AI in sectors based outside of London and the South East. This would focus on the commercialisation of ideas and could see, for example, the government focusing investment, researchers and developers to work in areas which currently do not use much AI technology but have potential, such as energy and farming. Publish a joint review with UKRI into the availability and capacity of computing power for UK researchers and organisations, including the physical hardware needed to drive a major roll out in AI technologies. The review will also consider wider needs for the commercialisation and deployment of AI, including its environmental impacts.
Johan den Haan is CTO of Mendix, a Siemens business and leader in enterprise low-code, a model-driven approach for building apps 10x faster. Is AI the transformative technology destined to work wonders for humanity, from driverless cars to a cure for cancer? Or is it a genie in a bottle that, once released, could be used to manipulate or even rule humankind? With the tremendous advances in computing power, software capabilities and the cloud over the last decade, progress on AI is no longer linear -- it's exponential. That means it's time to pay attention and make some fundamental decisions.
Before presenting employer certification-demand findings, it is necessary to describe the methodology in order to assist in interpreting the results. The certification-demand analysis was performed using the Economic Modeling Specialists International (EMSI) dataset. To populate the dataset, EMSI combs through 100,000 websites, effectively capturing job listings for more than 1.5 million companies. The same job listings regularly appear on multiple websites. To reduce duplicates, EMSI uses a machine learning-based duplicate-detection process.
The recent emergence of artificial intelligence (AI)-powered media manipulations has widespread societal implications for journalism and democracy,7 national security,1 and art.8,14 AI models have the potential to scale misinformation to unprecedented levels by creating various forms of synthetic media.21 For example, AI systems can synthesize realistic video portraits of an individual with full control of facial expressions, including eye and lip movement;11,18,34,35,36 clone a speaker's voice with a few training samples and generate new natural-sounding audio of something the speaker never said;2 synthesize visually indicated sound effects;28 generate high-quality, relevant text based on an initial prompt;31 produce photorealistic images of a variety of objects from text inputs;5,17,27 and generate photorealistic videos of people expressing emotions from only a single image.3,40 The technologies for producing machine-generated, fake media online may outpace the ability to manually detect and respond to such media. We developed a neural network architecture that combines instance segmentation with image inpainting to automatically remove people and other objects from images.13,39 Figure 1 presents four examples of participant-submitted images and their transformations. The AI, which we call a "target object removal architecture," detects an object, removes it, and replaces its pixels with pixels that approximate what the background should look like without the object.