Tech4TH Solutions

I’m happy to share that I’m starting a new position as President at Hapi!

Many a time, I have felt that my association with the travel industry was preordained. My father spent over four decades in the merchant navy (merchant marines) and, growing up, our home was filled with mementos from all over the world. The world of travel always excited me and when, five years into my working life, I got the opportunity to be a part of the travel industry by working with technology companies, I jumped right in and have stayed the course for over fifteen now. I am fortunate to have engaged with leading companies in the travel and hospitality space, primarily in the digital solutions arena. I love to create. This is evident while I cook new dishes with whatever ingredients are at arm’s length or when I tinker with complex Lego models over many nights. I’ve also realized that when presented with a problem, I find it difficult, if not impossible, to ignore it and move ahead. I MUST solve the problem, even it is sometimes an energy drainer. At work, this forces me to give meaning to the abstract and ideate to solve business challenges. Why does all this matter? Well, incidentally, the lessons I learned, the traits I developed, the work I did – all have a common connection which is my desire to “create something of my own, solve complex problems, and stay close to anything travel.”   And here, I land at Tech4TH. A company that I am proud to start with my two dear friends, Siva Vajjhala and Venkat Nandikolla, whom I’ve known for over a decade both personally and professionally. Despite our different personalities, what brings the three of us together is a shared value system and a belief of making a positive difference to the world of travel. It is nothing less than a dream come true. We have now set forth to create Tech4TH Solutions, a company that will leverage the power of imagination to create technology solutions for the travel and hospitality industry, the same way how a child would imagine without any boundaries. Look forward to your best wishes in my new path ahead on this wonderful journey! #traveltechnology #tech4thsolutions #travelindustry #hospitalitytechnology #digitalsolutions #reimaginetravel #returnonimagination #hospitalityindustry #travel #technology #digital

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” This year’s two Nobel Prize laureates in physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures. When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain’s neurons are represented by nodes that have different values. These nodes influence each other through connections that can be likened to synapses and which can be made stronger or weaker. The network is trained, for example by developing stronger connections between nodes with simultaneously high values. This year’s laureates have conducted important work with artificial neural networks from the 1980s onward. John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with. Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning. Learn more Press release: https://bit.ly/4gCTwm9 Popular information: https://bit.ly/3Bnhr9d Advanced information: https://bit.ly/3TKk1MM