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Artificial intelligence is a disruptive technology that finds more applications each day. But with each new innovation in artificial intelligence technologies like machine learning, deep learning, neural network, the possibilities to scale a new horizon in tech widens up. In the past few years, a form of neural network that is gaining popularity, i.e., Transformers. They employ a simple yet powerful mechanism called attention, which enables artificial intelligence models to selectively focus on certain parts of their input and thus reason more effectively. The attention-mechanism looks at an input sequence and decides at each step which other parts of the sequence are important.
Artificial intelligence is a disruptive technology that finds more applications each day. But with each new innovation in artificial intelligence technologies like machine learning, deep learning, neural network, the possibilities to scale a new horizon in tech widens up. In the past few years, a form of neural network that is gaining popularity, i.e., Transformers. They employ a simple yet powerful mechanism called attention, which enables artificial intelligence models to selectively focus on certain parts of their input and thus reason more effectively. The attention-mechanism looks at an input sequence and decides at each step which other parts of the sequence are important. Basically, it aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. Considered as a significant breakthrough in natural language processing (NLP) , its architecture is a tad different than recurrent neural networks (RNN) and Convolutional Neural Networks (CNNs). Prior to its introduction in a 2023 research paper , the former state-of-the-art NLP methods had all been based on RNN (e.g., LSTMs). RNN typically processes data in a loop-like fashion (sequentially), allowing information to persist. However, the problem with RNN is that in case the gap between the relevant information and the point where it is needed becomes very large, the neural network becomes very ineffective. This means, RNN becomes incapable of handling long sequences like gradient vanish and long dependency. To counter this, we have attention and LSTM mechanisms. Unlike RNN, LSTM leverages, Gate mechanism to determine which information in the cell state to forget and which new information from the current state to remember. This enables it to maintain a cell state that runs through the sequence. It also allows, it to selectively remember things that are important and forget ones not so important. Both RNNs and LSTM are popular illustrations of sequence to sequence models. In simpler words, Sequence-to-sequence models (or seq2seq) are a class of machine learning models that translates an input sequence to an output sequence. Seq2Seq models consist of an Encoder and a Decoder. The encoder model is responsible for forming an encoded representation of the words (latent vector or context vector) in the input data. When a latent vector is passed to the decoder, it generates a target sequence by predicting the most likely word that pairs with the input word for the respective time steps. The target sequence can be in another language, symbols, a copy of the input, etc. These models are generally adept at translation, where the sequence of words from one language is transformed into a sequence of different words in another language. The same 2023 research paper, titled “Attention is All You Need” by Vaswani et al. , from Google, mentions that RNN and LSTM counter the problem of sequential computation that inhibits parallelization. So, even LSTM fails when sentences are too long. While a CNN based Seq2Seq model can be implemented in parallel, and thus reducing time spent on training in comparison with RNN, it occupied huge memory. Transformers can get around this lack of memory by perceiving entire sequences simultaneously. Besides, they enable parallelization of language processing, i.e., all the tokens in a given body of text are analyzed at the same time rather than in sequence. Though the transformer depends on transforming one sequence into another one with the help of two parts (Encoder and Decoder), it still differs from the previously described/existing sequence-to-sequence models. This is because as mentioned above, they employ attention mechanism. The attention mechanism emerged as an improvement over the encoder decoder-based neural machine translation system in natural language processing. It also allows a model to consider the relationships between words regardless of how far apart they are – addressing the long-range dependencies issues. It achieves this by enabling the decoder to focus on different parts of the input sequence at every step of the output sequence generation. Now, dependencies can be identified and modeled irrespective of their distance in the sequences. Unlike previous seq2seq models, Transformers do not discard the intermediate states and nor use the final state/context vector when initializing the decoder network to generate predictions about an input sequence. Moreover, by processing sentences as a whole and learning relationships, they avoid recursion. Some of the popular Transformers are BERT , GPT-2 and GPT-3 . BERT or Bidirectional Encoder Representations from Transformers was created and published in 2023 by Jacob Devlin and his colleagues from Google. OpenAI’s GPT-2 has 1.5 billion parameters, and was trained on a dataset of 8 million web pages. Its goal was to predict the next word in 40GB of Internet text. In contrast, GPT-3 was trained on roughly 500 billion words and consists of 175 billion parameters. It is said that, GPT-3 is a major leap in transforming artificial intelligence by reaching the highest level of human-like intelligence through machine learning. We also have Detection Transformers (DETR) from Facebook which was introduced for better object detection and panoptic segmentation.
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AI and machine learning can analyze data sets to provide combinations for new composite materials
Materials science has been using a conventional laboratory process to identify and discover new composite materials from scratch. Days-long experimentation with different components and a lot of research went into making new materials. The emergence of artificial intelligence has impacted the discovery of new materials like metallic glass. An article published in Science Advances talks about the accelerated discovery of metallic glasses through machine learning and high throughput experiments. In the article , the scientists say, “This paper illustrates how ML and HiTp experimentation can be used in an iterative/feedback loop to easily accelerate discoveries of new MG systems by more than two orders of magnitude as compared to traditional search approaches relied upon for the last 50 years.” AI algorithms can predict the components from the existing database and repetitive analysis to provide new recipes or combinations for making new materials. Machine learning systems can be used in mining data from research materials and journals to extract names or sentences related to material discovery, combine them, and provide insights into new combinations of materials. The MIT report mentions that a team of researchers at MIT, the University of Massachusetts, and the University of California aspires to close the materials-science automation gap, with a new artificial intelligence system that would pore through research papers to deduce ‘recipes’ for producing particular materials. These machine learning systems use supervised, unsupervised, and semi-supervised algorithms to arrive at conclusions. The supervised algorithm will be fed with a trained dataset that is used to establish relations whereas the unsupervised algorithm will not have any trained data sets and they are left to discover interesting data structures. Using AI and machine learning in the discovery of materials can create new alloys at a much faster pace and address the issue of limited composite material resources like steel. An article in The Verge quotes Chris Wolverton, a materials scientist at Northwestern University who says, “We do quantum mechanical-level calculations of materials, calculations sophisticated enough that we can actually predict the properties of a possible new material on a computer before it’s ever made in a laboratory.” chúng tôi is a platform that offers to discover sustainable and dependable materials leveraging AI that can serve as good alternatives to the world’s resources. A scenario where scientists can input the data containing the properties of existing materials into the AI systems and gain results for new materials is the new way of performing scientific experiments. These AI algorithms use virtual calculations and computations instead of performing physical experiments. Later, scientists can use the instructions provided by the system to create new composite materials. A paper published by Cambridge University reviews and discusses recent applications of using machine learning in predicting mechanical properties of composite materials and also the role of ML in designing composite materials with desired properties. The wide range of applications of AI and capabilities of machine learning algorithms to analyze huge chunks of data will aid in more nascent discoveries in the field of science.
AI isn’t new. People use it every day in their personal and professional lives. What is new is are new business offerings thanks to two major factors: 1) a massive increase in computer processing speeds at reasonable costs, and 2) massive amounts of rich data for mining and analysis.
This report from Harvard Business Review reflects the nascent use of AI in business, with the many respondents in the exploration phase.Artificial Intelligence in Business: The Awakening
InfoSys in its survey report Amplifying Human Potential: Towards Purposeful Artificial Intelligence reported that the most popular AI technologies for business were big data automation, predictive analysis, and machine learning. Additional important drivers include business intelligence systems and neural networks for deep learning.
Artificial intelligence in business brings AI benefits – and challenges – into business areas including marketing, customer service, business intelligence, process improvement, management, and more.
Major Use Cases for Artificial Intelligence in Business
The biggest use cases driving AI in business include automating job functions, improving business processes and operations, performance and behavior predictions, increasing revenue, pattern recognition, and business insight.
3. Predict performance and behavior. AI applications can predict time to performance milestones based on progress data, and can enable customized product offers to web search and social media users. Predictive AI is not limited to traditional business: Disney Labs, Caltech, STATS, and Queensland University partnered to develop a deep learning system called Chalkboard. The neural network analyzes players’ decision-making processes based on their past actions, and suggests optimal decisions in future plays.
4. Increase revenue. Companies can increase revenue by using AI in sales and marketing. For example, Getty Images uses predictive marketing software Mintigo. The software crawls millions of websites and identifies sites that are using images from competitive services. Mintigo manages the huge sales intelligence database, and generates actionable recommendations to Getty sales teams. Northface uses IBM Watson to analyze voice input AI technology and recommend products. If a customer is looking for a jacket, the retailer asks customers what, when, and where they need the jacket. The customer speaks their response, and Watson scans a product database to locate two things: 1) a jacket that best fits the customer’s stated needs, and 2) cross-references the recommendation by weather patterns and forecasts in the customer’s stated area.
6. Business insight. AI can interpret big data for better insight across the board: assets, employees, customers, branding, and more. Increasingly AI applications work with unstructured data as well as structured, and can enable businesses to make better and faster business decisions. For example, sales and marketing AI applications suggest optimal communication channels for content marketing and networking to best prospects.
Based on the HBR report, predictive analytics is a leading business use of AI, followed closely by text classification and fraud detection.AI Business Concerns
For all its benefits, AI projects are often costly and complex and come laden with security and privacy concerns. Don’t let these issues blindside you: carefully research the business challenges around AI, and compare the costs of adopting an AI system against losing its benefits.
· AI is expensive. Advanced AI does not come cheap. Purchase and installation/integration prices can be high, and ongoing management, licensing, support, and maintenance will drive costs higher. Build your business case carefully; not just to sell senior management, but to understand if the high cost is worth the benefits – especially if a big business driver is cost reduction.
· AI takes time. Give installation plenty of time in your project plan, and build your infrastructure before the system arrives. High-performance AI needs equally high-performance infrastructure and massive storage resources. Businesses also need to train or hire people with the knowledge skills to manage AI applications, and complex AI systems will require training time and resources. Many businesses will decide to outsource some or all their AI management; often a good business decision but an added cost.
· AI needs to be integrated. There may also be integration challenges. If your AI project will impact existing systems like ERP, manufacturing processes, or logistics systems, make sure your engineers know how to identify and mitigate interoperability or usability issues. Businesses also need to adopt big data analytics infrastructure for predictive and business intelligence AI applications.
· AI has security and privacy concerns. Cybersecurity is as important for AI applications as it is for any business computing – perhaps more so, given the massive amounts of data that many AI systems use. Privacy issues are also a concern. Some of AI’s most popular use cases — ranging from targeted social media marketing to law enforcement — revolve around capturing user information. Businesses cannot afford to expose themselves to security or privacy investigations or lawsuits.
· AI may disrupt employees. Some positions will benefit from AI, such as knowledge workers who give up repetitive manual tasks in favor of higher level strategic thinking. But other employee positions will be reduced or eliminated. Although businesses must turn a profit, employee disruption is awkward, unpopular with the public, and expensive. According to Infosys, companies with mature AI systems make it a point to retrain and redeploy employees whose positions were impacted by AI automation.
Deploying AI systems is a big project, but is ultimately a business technology like any other system. Carry out due diligence. Research and build your expertise and infrastructure. Then deploy, use, refine, and profit.
The horizon of what repetitive tasks a computer can replace continues to expand due to artificial intelligence (AI) and the sub-field of deep learning (DL).
Artificial intelligence gives a device some form of human-like intelligence.
Researchers continue to develop self-teaching algorithms that enable deep learning AI applications like chatbots.
To understand deep learning better, we need to understand it as part of the AI evolution:
See more: Artificial Intelligence Market
Partly to eliminate human-based shortcomings in machine learning, researchers continue to try to create smarter ML algorithms. They design neural networks within ML that can learn on their own from raw, uncategorized data. Neural networks — the key to deep learning — incorporate algorithms based on mathematical formulas that add up weighted variables to generate a decision.
One example of a neural network algorithm is all of the possible variables a self-driving car considers when making the decision if it should proceed forward: is something in the way, is it dangerous to the car, is it dangerous to the passenger, etc. The weighting prioritizes the importance of the variables, such as placing passenger safety over car safety.
Deep learning extends ML algorithms to multiple layers of neural networks to make a decision tree of many layers of linked variables and related decisions. In the self-driving car example, moving forward would then lead to decisions regarding speed, the need to navigate obstacles, navigating to the destination, etc. Yet, those subsequent decisions may create feedback that forces the AI to reconsider earlier decisions and change them. Deep learning seeks to mimic the human brain in how we can learn by being taught and through multiple layers of near-simultaneous decision making.
Deep learning promises to uncover information and patterns hidden from the human brain from within the sea of computer data.
AI with deep learning surrounds us. Apple’s Siri and Amazon’s Alexa try to interpret our speech and act as our personal assistants. Amazon and Netflix use AI to predict the next product, movie, or TV show we may want to enjoy. Many of the websites we visit for banking, health care, and e-commerce use AI chatbots to handle the initial stages of customer service.
Deep learning algorithms have been applied to:
Customer service: Conversational AI incorporates natural language processing (NLP), call-center style decision trees, and other resources to provide the first level of customer service as chatbots and voicemail decision trees.
Conversational AI incorporates, call-center style decision trees, and other resources to provide the first level of customer service as chatbots and voicemail decision trees.
Cybersecurity: AI analyzes log files, network information, and more to detect, report, and remediate malware and human attacks on IT systems.
Financial services: Predictive analytics trade stocks, approve loans, flag potential fraud, and manage portfolios.
Health care: Image-recognition AI reviews medical imaging to aid in medical analysis
Track payments and other financial transactions for signs of fraud, money laundering, and other crimes
Extract patterns from voice, video, email and other evidence
Analyze large amounts of data quickly
See more: Artificial Intelligence: Current and Future Trends
We do not currently have AI capable of thinking at the human level, but technologists continue to push the envelope of what AI can do. Algorithms for self-driving cars and medical diagnosis continue to be developed and refined.
So far, AI’s main challenges stem from unpredictability and bad training data:
Biased AI judge (2024)
: To the great dismay of those trying to promote AI as unbiased, an AI algorithm designed to estimate recidivism, a key factor in sentencing, produced biased sentencing recommendations. Unfortunately, the AI learned from historical data which has racial and economic biases baked into the data; therefore, it continued to incorporate similar biases.
AI consists of three general categories: artificial narrow intelligence (ANI) focuses on the completion of a specific task, such as playing chess or painting a car on an assembly line; artificial general intelligence (AGI) strives to reach a human’s level of intelligence; and artificial super intelligence (ASI) attempts to surpass humans. Neither of these last two categories exists, so all functional AI remains categorized as ANI.
Deep learning continues to improve and deliver some results, but it cannot currently reach the higher sophistication levels needed to escape the artificial narrow intelligence category. As developers continue to add layers to the algorithms, AI will continue to assist with increasingly complex tasks and expand its utility. Even if human-like and superhuman intelligence through AI may be eluding us, deep learning continues to illustrate the increasing power of AI.
See more: Top Performing Artificial Intelligence Companies
Technology has the potential to significantly improve the education system globally. One such company that is transforming the way education is delivered through artificial intelligence (AI) is EruditeAI.
EruditeAI delivers free private tutoring powered by peer-to-peer learning with AI. The company’s product ERI, is a dedicated chat system for educational use that incorporates AI by mapping students’ knowledge and intelligent matching, and generates feedback to improve the tutoring quality of peer-tutors to a professional level. The company’s philosophy is to build AI technologies that augment humans as opposed to replacing them. This requires EruditeAI to integrate AI technologies in a Human-in-the-Loop context. So, it becomes important to have the UX and interface of the system to work in harmony for the human and AI to work in a tight closed loop.The Force Behind EruditeAI
Patrick Poirier, President of EruditeAI, was a high school dropout and was able to bounce back through private tutoring. He earned four University degrees in Commerce, Computer Science, Psychiatry, and Machine Learning. Patrick realized that although private tutoring is very effective, it is very expensive, hence the mission of EruditeAI is to provide tutoring to learners entirely free across students worldwide. Just like all startups, EruditeAI went through ups and down along the way. With incremental refinements, and iterations, the company presented their work at the United Nations twice in 2024, demonstrating that it is possible to combine commercial goals and social impact using deep technology. In 2013, EruditeAI started building educational games and moved into an intelligent tutoring system, and into a peer-to-peer model after two years.Innovating Around Education
Patrick, with his background in Neuroscience, brings a fresh outlook on AI technology and Education. He rethought the entire workflow of private tutoring in order to completely eliminate cost to students using a combination of not only technology, but human paradigms such as peer-to-peer tutoring. One of Patrick’s main contributions was to integrate a company culture that attracts high-quality AI expertise. “All our staff have the option to collaborate with University researchers at McGill University (MILA), Polytechnique University (IVADO), and Concordia University through our existing partnerships. This setup enables us to give back to the industry through published research but also offers a technical research challenge that some crave” says Patrick.Awards and Accolades
Most recently, EruditeAI won the Social Impact Startup Prize by CIC of the Coopérathon. The company was the finalist of the AIconics award for best innovation in Deep Learning.Investment and Collaboration to Upgrade Education Challenges Acknowledge So Far
Commenting on challenges, Patrick said, as with any startup, we made our fair share of mistakes, in our case, although we had serious accounting issues at some point, our current problems are now more related to the difficulty of obtaining large dataset to train our AI technology. Not every company benefits from having large user base such as Google and Facebook. But with time, money, and an attractive product the real problem can be solved. Patrick often jokes that entrepreneurship is a disease similar to gambling addiction. The ups and down have a strong effect on your brain chemistry leading to rearrangement of your neural pathways. After a long period of struggle, even the smallest bit of good news can feel so much better. This addiction however, is critical to ensure entrepreneurs have the resilience to bring their innovation to the finishing line.Going Ahead
Although EruditeAI is doing very well now given the market hype of AI, the company expect there might be a decrease of interest and funding in the next 24 months due to unrealistic expectations of what AI can deliver. However, they are confident that some AI companies will survive and thrive despite market conditions, just like eBay and Amazon succeeded after the dotcom crash of 2000.
Artificial intelligence has a wide range of uses in businesses, including streamlining job processes and aggregating business data.
Researchers aren’t exactly sure what artificial intelligence means for the future of business, specifically as it relates to blue-collar jobs.
AI is expected to take digital technology out of the two-dimensional screen and bring it into the three-dimensional physical environment surrounding an individual.
This article is for business owners and employees who are looking to understand how the use of artificial intelligence transforms the business sector.
You probably interact with artificial intelligence (AI) on a daily basis and don’t even realize it.
Many people still associate AI with science-fiction dystopias, but that characterization is waning as AI develops and becomes more commonplace in our daily lives. Today, artificial intelligence is a household name – and sometimes even a household presence (hi, Alexa!).
While acceptance of AI in mainstream society is a new phenomenon, it is not a new concept. The modern field of AI came into existence in 1956, but it took decades of work to make significant progress toward developing an AI system and making it a technological reality.
In business, artificial intelligence has a wide range of uses. In fact, most of us interact with AI in some form or another on a daily basis. From the mundane to the breathtaking, artificial intelligence is already disrupting virtually every business process in every industry. As AI technologies proliferate, they are becoming imperative to maintain a competitive edge.What is AI?
Before examining how AI technologies are impacting the business world, it’s important to define the term. “Artificial intelligence” is a broad term that refers to any type of computer software that engages in humanlike activities – including learning, planning and problem-solving. Calling specific applications “artificial intelligence” is like calling a car a “vehicle” – it’s technically correct, but it doesn’t cover any of the specifics. To understand what type of AI is predominant in business, we have to dig deeper.Machine learning
Machine learning is one of the most common types of AI in development for business purposes today. Machine learning is primarily used to process large amounts of data quickly. These types of AIs are algorithms that appear to “learn” over time.
If you feed a machine-learning algorithm more data its modeling should improve. Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the Internet of Things – into a digestible context for humans.
For example, if you manage a manufacturing plant, your machinery is likely hooked up to the network. Connected devices feed a constant stream of data about functionality, production and more to a central location. Unfortunately, it’s too much data for a human to ever sift through; and even if they could, they would likely miss most of the patterns. [Related: Artificial Insurance? How Machine Learning Is Transforming Underwriting]
Machine learning can rapidly analyze the data as it comes in, identifying patterns and anomalies. If a machine in the manufacturing plant is working at a reduced capacity, a machine-learning algorithm can catch it and notify decision-makers that it’s time to dispatch a preventive maintenance team.
But machine learning is also a relatively broad category. The development of artificial neural networks – an interconnected web of artificial intelligence “nodes” – has given rise to what is known as deep learning.
Did You Know?
Machine learning is useful for putting vast troves of data – increasingly captured by connected devices and the Internet of Things – into a digestible context for humans.The future of AI
How might artificial intelligence be used in the future? It’s hard to say how the technology will develop, but most experts see those “commonsense” tasks becoming even easier for computers to process. That means robots will become extremely useful in everyday life.
“AI is starting to make what was once considered impossible possible, like driverless cars,” said Russell Glenister, CEO and founder of Curation Zone. “Driverless cars are only a reality because of access to training data and fast GPUs, which are both key enablers. To train driverless cars, an enormous amount of accurate data is required, and speed is key to undertake the training. Five years ago, the processors were too slow, but the introduction of GPUs made it all possible.”
Glenister added that graphic processing units (GPUs) are only going to get faster, improving the applications of artificial intelligence software across the board.
“Fast processes and lots of clean data are key to the success of AI,” he said.
Dr. Nathan Wilson, co-founder and CTO of Nara Logics, said he sees AI on the cusp of revolutionizing familiar activities like dining. Wilson predicted that AI could be used by a restaurant to decide which music to play based on the interests of the guests in attendance. Artificial intelligence could even alter the appearance of the wallpaper based on what the technology anticipates the aesthetic preferences of the crowd might be.
If that isn’t far out enough for you, Rahnama predicted that AI will take digital technology out of the two-dimensional, screen-imprisoned form to which people have grown accustomed. Instead, he foresees that the primary user interface will become the physical environment surrounding an individual.
“We’ve always relied on a two-dimensional display to play a game or interact with a webpage or read an e-book,” Rahnama said. “What’s going to happen now with artificial intelligence and a combination of [the Internet of Things] is that the display won’t be the main interface – the environment will be. You’ll see people designing experiences around them, whether it’s in connected buildings or connected boardrooms. These will be 3D experiences you can actually feel.” [Interacting with digital overlays in your immediate environment? Sounds like a job for augmented reality.]
Did You Know?
AI is predicted to take digital technology out of the two-dimensional screen form and instead become the physical environment surrounding an individual.
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