AI-assisted modernization can reduce the time and complexity of transforming legacy systems while creating a foundation for faster innovation, says Asifa Sherazi, CIO of health insurance at Bupa and Sanjeev Tripathi, senior VP, region head of BFSI, healthcare, and public sector at Infosys. For years, legacy technology has been a problem companies knew they needed to solve, but one they often struggled to tackle. The cost, complexity, and risk of replacing business-critical systems could make modernization feel like a disruption to manage instead of an opportunity to pursue. But with the rise in customer expectations and the changes AI brought to the economics of software development, that calculation is changing. Bupa's modernization of its My Bupa mobile application offers a case study in what becomes possible when a legacy migration is treated as a business transformation rather than a technology rewrite. Bupa CIO of health insurance Asifa Sherazi describes the risks of waiting for legacy systems to become an emergency: "The end-of-life technology is a risk that compounds quietly, and then arrives all at once." For Bupa, moving its application from Xamarin to native Swift and Kotlin improved the app rating from 3.7 to 4.7, while the user-perceived crash rate fell by nearly 24 percentage points on Android and eight points on iOS. "What they'll notice is that when they need us, often at a stressful moment, it just simply works," Sherazi says. Sanjeev Tripathi, senior vice president and region head of BFSI, healthcare, and public sector for Australia, New Zealand, and Southeast Asia at Infosys, contends that AI is helping change the equation. "The emergence of AI is fundamentally shifting the economics of modernization," he says, reducing the effort, risk, and time traditionally associated with these programs. At Bupa, combining AI-assisted reverse engineering with forward engineering helped deliver the transformation in approximately 60% less time than would have been possible in the pre-AI era.

Running from 1992 through 1995, Ghostwriter, the beloved PBS children's television show, followed a diverse group of friends as they solved mysteries around their Brooklyn neighborhood with the help of their haunted typewriter, a cursed item possessed by the trapped soul of a murdered runaway Civil War slave. The Ghostwriter typewriter developed by interaction designer, artist and Lumen.world CTO, Arvind Sanjeev, on the other hand, comes with none of the paranormal hang-ups of its coincidental namesake. Instead of a spirit bound to this hellish plane of existence, forced to help tweens solve low-stakes conundrums, the deus in Sanjeev's machina is animated by OpenAI's GPT-3. He first devised this artistic endeavor in 2021 as a, "poetic intervention that allows us to take a moment to breathe and reflect on this new creative relationship we are forming with machines."

On Wednesday, a designer and engineer named Arvind Sanjeev revealed his process for creating Ghostwriter, a one-of-a-kind repurposed Brother typewriter that uses AI to chat with a person typing on the keyboard. The "ghost" inside the machine comes from OpenAI's GPT-3, a large language model that powers ChatGPT. The effect resembles a phantom conversing through the machine. To create Ghostwriter, Sanjeev took apart an electric Brother AX-325 typewriter from the 1990s and reverse-engineered its keyboard signals, then fed them through an Arduino, a low-cost microcontroller that is popular with hobbyists. The Arduino then sends signals to a Raspberry Pi that acts as a network interface to OpenAI's GPT-3 API.

On Wednesday, a designer and engineer named Arvind Sanjeev revealed his process for creating Ghostwriter, a one-of-a-kind repurposed Brother typewriter that uses AI to chat with a person typing on the keyboard. The "ghost" inside the machine comes from OpenAI's GPT-3, a large language model that powers ChatGPT. The effect resembles a phantom conversing through the machine. To create Ghostwriter, Sanjeev took apart an electric Brother AX-325 typewriter from the 1990s and reverse-engineered its keyboard signals, then fed them through an Arduino, a low-cost microcontroller that is popular with hobbyists. The Arduino then sends signals to a Raspberry Pi that acts as a network interface to OpenAI's GPT-3 API.

Sanjeev is a recipient of the 40 Under 40 Data Scientists award, at the Machine Learning Dev Summit for his research in autonomous tech.

Artificial Intelligence (AI) has been hailed to be the next saviour of the NHS, with new robotic overlords saving thousands of lives from cancer-related deaths over the next 15 years. AI has suddenly become panacea, solving everything from self-driving cars to detecting fake news - but what changed so fast? On one hand, not much: most of the hype that people are selling as "AI" was only a few years ago termed "machine learning", a change in part due to a previous desire to distance humans from something that was indistinguishable from one. On the other hand, a few specific advancements have aligned, computational power has increased and data is being generated at an unfathomable rate, with more created in the 2017 than in the history of mankind. In other words, it isn't the ideas or the technologies that are new, but rather the opportunities.

He is the founder, CEO, and lead designer of SpaceX; co-founder, CEO, and product architect of Tesla, Inc.; and co-founder and CEO of Neuralink He was ranked the No. 1 global FinTech influencer and the No. 2 InsurTech influencer by Onalytica. He is a senior advisor at Arbidex, Glance Technologies, Datametrex AI, kapilendo.de, She is the director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab. Since age 15, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks (since 1991) such as Long Short-Term Memory (LSTM) have revolutionised machine learning, and are now available to billions of users through the world's most valuable public companies, e.g., for greatly improved speech recognition on over 2 billion Android phones, greatly improved machine translation through Google (since 2016) and Facebook (over 4 billion LSTM-based translations per day as of 2017), Apple's Siri and Quicktype on almost 1 billion iPhones (since 2016), the answers of Amazon's Alexa, and numerous other applications.