A data-centric strategy seeks to ensure high-quality data input rather than tinkering with model settings
To increase the precision of AI systems, the data-centric approach involves methodically optimising datasets. This strategy is considered promising by machine learning experts since refined data produces superior results to raw data. A data-centric strategy seeks to ensure high-quality data input rather than tinkering with model settings.
The training data used in machine learning consists of labelled pictures, words, audio files, videos, and other types of data. The developed model and its optimization will perform badly if the training data is subpar. Through AI-based chatbots, this might result in terrible consumer experiences, but in a biological algorithm or an autonomous car, it might be fatal.
Data Annotation Platform
Data quality depends on exact, accurate, and consistent annotation creation. You cannot create a model correctly if your data are not correctly labelled. You won’t be able to create a robust model if your data amount is insufficient. Data annotation, however, involves more than simply the quantity and quality of labelled data; it also involves the type of tags you use for the models you’re creating. After all, even if we continued with the “model-centric” strategy, our model would remain static without providing best-in-class data labelling. This is the first step in developing a computer vision model: high-quality, scaleable labelled data. Whether you’re performing detection, segmentation, or classification, you must annotate your data before building a computer vision model.
A data-stable strategy is one that is data-centric. This indicates that you are handling your data’s whole lifecycle. You need to monitor the development of your dataset even before you create your model. Your datasets must be able to be filtered, sorted, copied, combined, versioned, and queried right down to the metadata level. As your AI project progresses, providing a single safe visualisation layer for all of your unstructured data will help you better comprehend the mountain of acquired data. Data engineers, data scientists, and data operators may evaluate data sets more rapidly and effectively with the help of robust tools.
Automation & Pipelines
The power to automate your analysis and data management routines will probably be the most crucial component of effectively maintaining a data-centric versus model-centric strategy as you ultimately grow your AI project. Being capable of pre- and post-processing your datasets is just as important as releasing your models into production. The key is being able to grow your work as you rewrite and optimise your constantly-adapting models and being able to generate human-in-the-loop data validation. With the help of Dataloop’s solution, businesses can build unique data automation pipelines that combine machine learning and human labelling jobs using a drag-and-drop interface with no programming required.
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Top 10 AI Companies That Are Revolutionizing The Education Ecosystem In 2022
These AI companies are transforming the education industry to impart effective knowledge
Machine learning and artificial intelligence are the primary drivers of growth and innovation across all industries, and the education sector is no different. AI-powered solutions that are being integrated into the edtech space are making things quite easy for educators. Virtual learning has been quite effective for modern students. Here, we have enlisted the top AI companies that are revolutionizing the education domain in 2022.
Osmo is known for merging tactile exploration with innovative technology, actively engaging children in the learning process. The main aim is to make children interact with technology without losing the value of hands-on play. Osmo has expanded its family of games to include Numbers, Masterpiece, Coding Awbie, Monster, and Pizza Co., incorporating unique characters, such as Awbie, Mo the Monster, and the Pizza Co. gang.
Carnegie Learning is a comprehensive, dynamic, and progressive learning technology company. Advocating a belief in teaching and determination to help students develop as learners and thinkers, Carnegie Learning is seeking to redefine the role of technology across the K-12 landscape. It delivers research-proven, high-quality core and supplemental solutions in math, world languages, ELA and literacy, computer science, and biotech, as well as best-in-class K-12 professional learning services.
Using learning science, artificial intelligence, and neuroscience, Century Tech creates constantly adapting pathways for students and powerful assessment data for teachers. It helps to boost learning with intelligent personalization to enhance student engagement and deep understanding efficiently and effectively. The actionable data insights tend to support targeted interventions seamlessly with cutting-edge technologies.
Knewton puts achievement within reach for all learners with adaptive technologies and products that deliver personalized and lasting learning experiences. Educators, schools and universities, and education companies around the world use Knewton to power and provide digital courses that dynamically adapt to each student’s unique needs. More than 14 million students around the world have used Knewton-powered courses to date. It designs the best adaptive technology that delivers lasting impact to put achievement within reach for all.
Memrise is an award-winning language learning system with over 50 million users. Specializing in combining cognitive science, powerful tech, and entertaining content, Memrise makes language learning genuinely recreational. It offers 200 language combinations across 24 languages on its website, and, iOS and Android apps. By leveraging lots of brain science and plenty of humor, the team is striving to enrich people’s consciousness and help people achieve confident, real-world language skills in just a few short months.
Novakid is a next-generation early English learning platform that allows kids around the world to learn English through remote practice with native-speaking teachers and engagement with AI-powered games and training apps. These modern technologies can promote English speaking and comprehension based on the use of authentic content in the English language. It uses an immersive approach to language learning making it easier for kids to maintain focus and gain knowledge efficiently and effectively.
Alibaba Cloud develops highly scalable cloud computing and data management services providing large and small businesses, financial institutions, governments, and other organizations with flexible, cost-effective solutions to meet their networking and information needs. A business of Alibaba Group, one of the world’s largest e-commerce companies, Alibaba Cloud operates the network that powers Alibaba Group’s extensive online and mobile commerce ecosystem and sells a comprehensive suite of cloud computing services to support sellers and other third-party entities participating in this ecosystem.
Practically is India’s first experiential learning app designed to make learning immersive for students of classes 6-12. It offers a comprehensive library of more than 3000 world-class 3D videos, and over 1000 AR experiences and simulations. It is the world’s first EdTech company to launch the revolutionary #ScanAnything feature with live classes, dedicated mentor support, study plan, test prep, and coding++ course along with an AI Study Buddy Proton, 24×7 Seek Help feature for doubt resolution.
Mindler is a comprehensive career guidance and mapping platform shaping the career guidance landscape by empowering students, educators, and parents. Using automated career guidance tools and solutions blended with in-depth research it is empowering educators and students to adopt a scientific approach to career decision-making. It is a venture conceptualized and run by alumni of ISB, IIT, IIM, Harvard, and the world’s preeminent psychometricians by leveraging cutting-edge technologies like big data analytics and machine learning.
EMBIBE is the most powerful education platform ever invented, that brings together world-class content, aligned to every prescribed school and test prep curriculum, in every language. It has patented artificial intelligence that forensically tracks and fixes gaps in learning, behavior, and skills ensuring your performance always matches your maximum potential. It helps to experience how concepts come to life with a wide range of exciting content through 3D technology.
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10 Most Promising AI Companies Changing The Financial Services Industry
These are the top AI companies that are radically changing the financial services domain
There are no business sectors in the world that have not been transformed by artificial intelligence. AI is responsible for speed, accuracy, and efficiency in delivering financial services. The AI revolution includes machine learning algorithms and software that trigger growth and development in the financial industry. Here are the top AI companies that are changing the financial sector in 2022.
HighRadius offers cloud-based Autonomous Software for the Office of the CFO. HighRadius Autonomous Software combines the best of both worlds to deliver measurable business outcomes such as DSO reduction, working capital optimization, bad-debt reduction, reduce month close timelines, and improved productivity in under six months. It brings modern digital transformation capabilities like Artificial Intelligence, Robotic Process Automation, Natural Language Processing, and Connected Workspaces as out-of-the-box features for the finance & accounting domain.
Signifyd provides an end-to-end commerce protection platform that leverages its commerce network to maximize conversion, automate customer experience and eliminate fraud and consumer abuse for retailers. It is the leading provider of payment security and fraud prevention for companies across the world. It offers fraud protection, abuse prevention, account protection, and payment optimization efficiently and effectively.
Darktrace is a global leader in cybersecurity and AI-focused on delivering world-class technology that protects over 6,800 customers worldwide from advanced threats, including ransomware and cloud and SaaS attacks. The company’s fundamentally different approach applies self-learning AI to enable machines to understand the business to autonomously defend it. It helps to rely on a digital immune system to avoid cyber disruptions without creating a huge impact on regular business operations.
Numerai manages an institutional-grade long/short global equity strategy for the investors in a hedge fund. It transforms and regularizes financial data into machine learning problems for the global community of data scientists. The most accurate and original machine learning models from the world’s best data scientists are synthesized into a collective artificial intelligence that controls the capital in Numerai’s hedge fund.
Celo is a mobile-first blockchain optimized for peer-to-peer payments using only a mobile number. An Ethereum-compatible technology capable of reaching global users at scale, Celo is turning crypto into usable money with a multi-asset system: governance and staking asset (CELO) and a family of stablecoins (e.g., USD, cEUR). Since the launch of mainnet in 2020, Celo’s network now supports 1000+ projects from builders, developers, and even artists, who every day create new applications and issue digital currencies from over 100 countries around the world.
ZestFinance is focused on doing a more profitable lending system by leveraging cutting-edge technologies such as machine learning and artificial intelligence. It offers Zest Automated Machine Learning or ZAML software to provide the only solution for explainable AI in credit and to automate risk management efficiently and effectively. It powers credit and underwriting at global financial firms by consuming vast amounts of data. ZestFinance utilizes machine learning and data science to help companies make more accurate credit decisions.
AlphaSense is a market intelligence platform used by the world’s leading companies and financial institutions. Since 2011, its AI-based technology has helped professionals make smarter business decisions by delivering insights from an extensive universe of public and private content—including company filings, event transcripts, news, trade journals, and equity research.
Signifyd provides an end-to-end Commerce Protection Platform that leverages its Commerce Network to maximize conversion, automate customer experience and eliminate fraud and consumer abuse for retailers. Signifyd’s customers appear on the Fortune 1000 and Digital Commerce 360 Top 1000 lists. Digital Commerce 360 also named Signifyd the leading provider of payment security and fraud prevention for the Top 1000 Retailers for 2022.
SAP is a technology company that develops enterprise application software for companies and industries across diverse sectors. The company offers solutions covering various lines of businesses, including asset management, commerce, finance, human resources, manufacturing, marketing, sales, services, sourcing and procurement, supply chain, and sustainability as well as research and development, and engineering. It provides enterprise application software to various industries, including consumer, discrete manufacturing, public services, energy, and natural resources, financial services, and many other services.
CAPE Analytics uses deep learning and geospatial imagery to provide instant property intelligence for buildings across the United States. CAPE enables insurers and other property stakeholders to access valuable property attributes at the time of underwriting—with the accuracy and detail that traditionally required an on-site inspection, but with the speed and coverage of property record pre-fill.
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Nanomagnetic Computing Is Here To Save AI’s Energy Usage
Nanomagnetic computing depends on nanomagnet networks for AI to make time-series predictions
“How the magnets interact gives us all the information we need; the laws of physics themselves become the computer.” Kilian Stenning quotes in his research paper “Reconfigurable training and reservoir computing in an artificial spin-vortex ice via spin-wave fingerprinting”, published in Nature magazine. He made this statement in the paper in an effort to explain how magnet spins can be put to use to power AI systems. What is that motivated Killian and the team to apply magnetic energy to make AI predictions? Artificial intelligence, by its very versatility, has become an indispensable technology even if it is largely at the nascent stage. The interesting part is, that its acceptance is because of the predictions it can make, ignoring the carbon footprint it has. The fact is that even the simplest decision taken by AI or ML algorithm consumes a lot of energy. Higher the efficiency of the model higher the energy consumption. To put things in perspective, let us consider the example of Megatron ML, a language model similar to GPT-3, trained on 45 terabytes of data, NVIDIA had to run 512 V100 GPUs for nine days. Considering the fact that a V100 GPU consumes approximately 250 watts, the project must have required around 27,648 kWh for training, whereas an average household requires 10,649 kWh annually.
A team led by Imperial College of London researchers has found a new method of harnessing magnetic energy, to simulate the computations the conventional neural networks of an ML system undergo. The method they discovered depends on nanomagnet networks for AI to make time-series predictions. Initially, the mathematical computations required to design the neural networks were based on the principles of magnetic interactions, and now scientists have found a way to apply them directly.
Variability in Nanomagnetic states holds the key
A Nanomagnetic state can be understood as a spin value a particular magnet takes in an excited state. When a group of nanomagnets is placed in an energy field, the magnets pass through different states of spin, and there is a pattern they interact with one another – scaling up the spins into nanopatterned arrays. To make a prediction, the scientists said, they designed a technique to count the number of magnets in a particular state after the field has passed through, and obtain the answer. When compared to electrical computations that consume energy whenever electrons pass through the energy field, nanomagnets’ stake in energy is negligible. Dr. Jack Gartside, the Co-first author of the study said, “We’ve been trying to crack the problem of how to input data, ask a question, and get an answer out of magnetic computing for a long time. Now we’ve proven it can be done, it paves the way for getting rid of the computer software that does the energy-intensive simulation.”
New found possibilities for Edge AI computation
Real-time processing leveraging the power of Edge AI is going to go mainstream very soon for automation and artificial intelligence to be of any value to humanity. Edge AI, while bringing in low latency, can significantly reduce network traffic, it is crippled by challenges so overwhelming that companies do not even think about it. Edge AI depends largely on hardware, which market doesn’t have the capacity to produce the standardized units to support the energy-guzzling AI algorithms. In addition, it requires seamless hardware integration, a tedious task, that the current equipment can least handle. The nanomagnetic computation with magnets integrated into conventional computers can upscale the energy efficiency of the system significantly.
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