One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Since their introduction in 2017, transformers have become the go-to machine learning architecture for natural language processing (NLP) and computer vision. Although they have achieved state-of-the-art performance in these fields, the theoretical framework underlying transformers remains relatively underexplored. In the new paper A Probabilistic Interpretation of Transformers, ML Collective researcher Alexander Shim provides a probabilistic explanation of transformers' exponential dot product attention and contrastive learning based on distributions of the exponential family. An oft-proposed explanation for transformers' power and performance is their attention mechanisms' superior ability to model dependencies in long input sequences. But this doesn't directly address how and why transformer architecture choices such as exponential dot product attention outperform the alternatives.
Blockchain is the new talk of the town. It is the technology behind cryptocurrencies like Bitcoin. Today, it has turned out to be a game-changer for businesses. Its decentralized ledger offers transparency and immutability in transactions between parties without any intermediary. The transactions are irreversible, which means once a ledger is updated, it can never be changed or deleted. Blockchain technology will eventually find its space in the new and innovative applications of Machine Learning and Artificial Intelligence.
Do not be swayed by the dulcet dial-tones of tomorrow's AIs and their siren songs of the singularity. No matter how closely artificial intelligences and androids may come to look and act like humans, they'll never actually be humans, argue Paul Leonardi, Duca Family Professor of Technology Management at University of California Santa Barbara, and Tsedal Neeley, Naylor Fitzhugh Professor of Business Administration at the Harvard Business School, in their new book The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI -- and therefore should not be treated like humans. The pair contends in the excerpt below that in doing so, such hinders interaction with advanced technology and hampers its further development. Reprinted by permission of Harvard Business Review Press. Excerpted from THE DIGITAL MINDSET: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI by Paul Leonardi and Tsedal Neeley.
On Friday, Elon Musk announced he was pausing his $45bn purchase of Twitter because he had only just discovered some of the accounts on the site were fake. But that's not the strangest thing that has happened to the beleaguered social media platform this week. Because on Tuesday the current top brass, perhaps trying to demonstrate their vision for the site, released a Super Nintendo-style browser game that recaps Twitter's private policy. The platform unveiled Twitter Data Dash, which plays like a vintage side-scrolling platformer that's been draped with a healthy dose of disinformation anxiety. You take control of a blue-hued puppy named Data and are tasked with retrieving five bones hidden in each of the game's day-glo urban environments.
The retail business is getting back on track and has been witnessing steady growth after the dismal impact of the third wave. There has been buoyancy in the market with the removal of lockdown restrictions. After a long time of distress and uncertainty, things are getting back to normalcy as businesses have started taking pertinent steps to resume operations and focus on sales, marketing, and inventory management. The realization of digital transformation coupled with the indispensable role of artificial intelligence (AI) has been one of the major outcomes of Covid-19 implications on the retail sector and the vast possibilities and opportunities it can create with such transformations. With the emergence of e-commerce, buyers experienced the first crucial shift that successfully made it possible for them to buy things from anywhere at any time.
Whether it's a romantic weekend away or a relaxing spa break, many of us have enjoyed being able to travel again following the Covid-19 pandemic. If you're planning any holidays, Google Maps' latest feature could be just the thing to make sure the destination passes the'vibe' check first. The app has launched a new'immersive view' tool that combines Street View and aerial images to allow you to virtually explore neighbourhoods. 'With our new immersive view, you'll be able to experience what a neighbourhood, landmark, restaurant or popular venue is like -- and even feel like you're right there before you ever set foot inside,' Miriam Daniel, VP of Google Maps, explained. 'So whether you're traveling somewhere new or scoping out hidden local gems, immersive view will help you make the most informed decisions before you go.'
The models can have many hyperparameters and finding the best combination of the parameter using grid search methods. Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid. We might use 10 fold cross-validation to search the best value for that tuning hyperparameter. These values are called hyperparameters. To get the simplest set of hyperparameters we will use the Grid Search method.
The Transformer soon became the most popular model in NLP after its debut in the famous article Attention Is All You Need in 2017. The capacity to analyze text in a non-sequential manner (as opposed to RNNs) enabled large models to be trained. The introduction of an attention mechanism proved tremendously valuable in generalizing text. Before the advent of Deep Learning, previous approaches to NLP were more rule-based, with simpler (pure statistical) machine learning algorithms being taught the words and phrases to look for in the text, and particular replies being created when these phrases were discovered. Following the publication of the study, numerous popular transformers emerged, the most well-known of which is GPT (Generative Pre-trained Transformer).
During the pandemic especially, it's become overwhelming for small- and medium-sized businesses (SMBs) to answer all of their customer service requests. A Freshworks survey found that companies experienced a 71% increase in overall contact volume between February 2020 and January 2021, and expect it to increase further. At the same time, customers -- while empathetic -- have become more demanding. The same poll shows that 68% of customer service managers have seen an increase in customer expectations. What's a company to do? Automation is one route to more manageable customer experience workloads, potentially.
Artificial Intelligence (AI) has been around since the early 1900s, but it's recently been applied to business in ways that many people never imagined. Artificial Intelligence (AI) is one of the popular innovations that businesses can utilize to improve operations, save money, and expand their reach. From customer service chatbots to warehouse robots and self-driving vehicles, AI promises to make tasks faster, easier, and more efficient than ever before. Automation makes repetitive tasks easier, saving time and money. For example, artificial intelligence can be used to schedule meetings and alert employees of potential conflicts.