Digital Transformation moved at a relatively slow pace for the past ten years, mainly focusing on improving products, employee experience and processes. But then, after COVID – 19 hit, IT decision-makers were forced to prioritize their IT initiatives in order to increase digital investments. According to IDC, over the next four years, worldwide Digital Transformation technology investment is set to reach at least $7.4 trillion and will be the first time that DX will account for the majority of IT spending – predicted to be a huge 53% of budgets. Digital transformation is a set of methodologies and tools which are used by modern companies to optimize their operational activities, such as increasing their reach power, providing differentiated service and increasing performance. However, digital transformation is not just a new department in the firm, but it is definitely a game-changer in technology's role in the corporate environment. That's why it is increasingly being seen as the 4th Industrial Revolution. "Think of digital transformation less as a technology project to be finished than as a state of perpetual agility, always ready to evolve for whatever customers want next, and you'll be pointed down the right path."-

The benefits of analytics are well-documented. Analytics has helped organisations transform retail experiences, map pathways for trains and trucks, discover extraterrestrial life, and even predict diseases. However, over the past few years, organisations across the globe have wrestled with just how much human error has permeated their analytics attempts, often ending with disastrous results. From crashing spacecraft to sinking ships, transferring billions of dollars to unintended recipients, and causing deaths due to overdose of medication, human error in data analysis has far-reaching ramifications for organisations. The reason for human error in data analysis could be many, such as lack of experience, fatigue or loss of attention, lack of knowledge, or the all-too-common biases in interpreting data. However, what's common among these errors is that they are related to humans reading, processing, analysing, and interpreting data.

Created the world's first internet cafe. Meet Minho Jung, the founder of Sahara Street LLC, known as the creator of the PC room. I understand that you applied for a patent related to Metaverse 20 years ago. As you know, Metaverse is emerging as the hottest potato these days, and I am curious about the patent background. In fact, in 20 years, it was known as AR and VR.

"A model is as good as the underlying data," said Jayachandran Ramachandran, SVP of Artificial Intelligence Labs at Course5 Intelligence during his MLDS talk "Will evolving regulations stymie AI innovations? He discussed how industries and governments recognise this problem and develop regulations and recommendations. He also touched on the recommendations and implications crelated to European Union's AI regulations draft. Today, most countries have an AI policy and strategies in place. The EU is at the forefront of AI regulations and drafts. "The EU draft in 2021 is acting as a benchmark for other countries," Ramachandran noted. The draft seeks to ensure the AI policy is human-centric, sustainable, secure, inclusive and trustworthy. Additionally, the draft focuses on a seamless transition of AI from the lab to the market. Any system deployed for the users based in the EU will be under the scope of this AI regulation. If the consumers are based outside the EU, they will not be held ...

Post-processing immunity is a fundamental property of differential privacy: it enables arbitrary data-independent transformations to differentially private outputs without affecting their privacy guarantees. Post-processing is routinely applied in data-release applications, including census data, which are then used to make allocations with substantial societal impacts. This paper shows that post-processing causes disparate impacts on individuals or groups and analyzes two critical settings: the release of differentially private datasets and the use of such private datasets for downstream decisions, such as the allocation of funds informed by US Census data. In the first setting, the paper proposes tight bounds on the unfairness of traditional post-processing mechanisms, giving a unique tool to decision-makers to quantify the disparate impacts introduced by their release. In the second setting, this paper proposes a novel post-processing mechanism that is (approximately) optimal under different fairness metrics, either reducing fairness issues substantially or reducing the cost of privacy. The theoretical analysis is complemented with numerical simulations on Census data.

It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency. We consider two variants of decision making, where "good" decisions amount to alternatives (i) meeting "most" goals, and (ii) meeting "most preferred" goals. We then define, for each variant and notion of "goodness" (corresponding to a number of existing notions in the literature), explanations in two formats, for justifying the selection of an alternative to audiences with differing needs and competences: lean explanations, in terms of goals satisfied and, for some notions of "goodness", alternative decisions, and argumentative explanations, reflecting the decision process leading to the selection, while corresponding to the lean explanations. To define argumentative explanations, we use assumption-based argumentation (ABA), a well-known form of structured argumentation. Specifically, we define ABA frameworks such that "good" decisions are admissible ABA arguments and draw argumentative explanations from dispute trees sanctioning this admissibility. Finally, we instantiate our overall framework for explainable decision-making to accommodate connections between goals and decisions in terms of decision graphs incorporating defeasible and non-defeasible information.

Manan Shah is the co-founder and CEO of Avalance Global Solutions, California-based cybersecurity, and breach and attack simulation company. Ever since Alan Turing helped decode the Enigma messages used by the Germans during World War II, the concept of artificial intelligence started getting traction. It was only in 1956 that the term actually was officially coined by none other than John McCarthy. It was the era when the debate over artificial intelligence began and became a heated topic. The concept fascinated a lot of free thinkers and frightened others.

The following is a partially redacted and lightly edited transcript of a chat conversation about AGI between Eliezer Yudkowsky and a set of invitees in early September 2021. By default, all other participants are anonymized as "Anonymous". I think this Nate Soares quote (excerpted from Nate's response to a report by Joe Carlsmith) is a useful context-setting preface regarding timelines, which weren't discussed as much in the transcript: The gap between AI systems then and AI systems now seems pretty plausibly greater than the remaining gap, even before accounting the recent dramatic increase in the rate of progress, and potential future increases in rate-of-progress as it starts to feel within-grasp. But basically all that has fallen. The gap between us and AGI is made mostly of intangibles. Sure, but on my model, "good" versions of those are a hair's breadth away from full AGI already. And the fact that I need to clarify that "bad" versions don't count, speaks to my point that the only barriers people can name right now are intangibles.) That's a very uncomfortable place to be! But I'm in the second-to-last epistemic state, where I wouldn't feel all that shocked to learn that some group has reached the brink. Maybe I won't get that call for 10 years! But it could also be 2, and I wouldn't get to be indignant with reality. I wouldn't get to say "but all the following things should have happened first, before I made that observation". I have made those observations. For one thing, I don't expect to need human-level compute to get human-level intelligence, and for another I think there's a decent chance that insight and innovation have a big role to play, especially on 50 year timescales. There has been a lot of AI progress recently.

On today's show: • Donna Shore and Colleen Sisk of Lena's Wood-Fired Pizza and The Loft at Lena's Seasonal Dining Experience; • Bryce Iapicca, director of operations, Puro Gusto, Washington, D.C., an authentic all-day Italian café based on the habits and rituals of the Italian people, from breakfast'til before dinner time. That is, from a quick morning coffee to a leisurely sit-down lunch to a sophisticated cocktail at the end the day; • Makenna Held, owner of The Courageous Cooking School, housed in what was Julia Child's La Pitchoune home in France .We chat about its culinary approach to a recipe-free way to learn to cook French and farm-to-table cuisine; Katerina Axelsson is the 30-year-old founder and CEO of Tastry, a California sensory sciences company. Tastry is an artificial intelligence and data insights company that predicts how consumers will perceive any product you can taste or smell. Tastry "taught a computer how to taste" and built the most sophisticated sensory product and consumer database to answer questions current technology could not answer. John Henry, operations officer of the USCG Cyber Command, discusses how the Command prepares for and responds to cyber incidents.

It is critical for governments, leaders, and decision makers to develop a firm understanding of the fundamental differences between artificial intelligence, machine learning, and deep learning. Artificial intelligence (AI) applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, and decision trees. AI recognizes patterns from vast amounts of quality data providing insights, predicting outcomes, and making complex decisions. Machine learning (ML) is a subset of AI that utilises advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon's Alexa and Apple's Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background.