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Category Archives: Machine Learning

Top 3 Applications Of Machine Learning To Transform Your Business – Forbes

We all hear about Artificial intelligence and Machine learning in everyday conversations, the terms are becoming increasingly popular across businesses of all sizes and in all industries. We know AI is the future, but how can it be useful to businesses today? Having encountered numerous organisations that are confused about the actual benefits of Machine Learning, AI experts agree it is necessary to outline its key applications in simple terms that most companies can relate to.

Here are the three most impactful Machine Learning applications that can transform your business today.

Machine learning can be used to automate a host of business operations, such as document processing, database analysis, system management, employee analytics, spam detection, chatbots. A lot of manual, time consuming processes can now be replaced or at least supported by off-the-shelf AI solutions. For those companies with unique requirements, looking to create or maintain a competitive advantage or otherwise prefer to retain control of the intellectual property (IP), it is worth reaching out to end-to-end service providers that can assist in planning, developing and implementing bespoke solutions to meet these business needs.

The reason why machine learning often ends up performing better than humans at a single task is that it can very quickly improve its performance through analysing vast amounts of historical data. In other words, it can learn from its own mistakes and optimise its performance very quickly and at scale. There is no ego and no hard feelings involved, simply objective analysis, enabling optimisation to be achieved with a high efficiency and effectiveness. Popular examples of optimisation with machine learning can be found around product quality control, customer satisfaction, storage, logistics, supply chain and sustainability. If you think something in your business is not running as efficiently as it could and you have access to data, machine learning may just be the right solution.

Companies are inundated with data these days. Capturing data is one thing, but navigating and extracting value from big, disconnected databases containing different types of data on different areas of your organisation adds complexity, cost, reduces efficiency and impedes effective decision making. Data management systems can help create clarity and put your data in order. You would be surprised how much valuable information can then be extracted from your data using machine learning. Typical applications in this space include churn prediction, sales forecasting, customer segmentation, personalisation, or predictive maintenance. Machine learning can teach you more about your organisation in a month than you have learned over the past year.

If you think one of the above applications might be helpful to your business now is a good time to start. As much as companies are reluctant to invest in innovation and new technologies, especially due to difficulties caused by Covid-19, it is important to recognise that the afore mentioned applications can bring a long-term benefits to your business such as cost savings, increased efficiency, improved operations and enhanced customer value. Get started and become a leader in your field thanks to the new machine learning technologies available to you today.

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NeuralCam Launches NeuralCam Live App Using Machine Learning to Turn iPhones into Smart Webcams – MarkTechPost

An era of virtual learning, when interviews, education, etc. are being conducted from home through laptops and the internet. The clarity of the camera for video calls, maybe work or class calls are the primary need of the hour. But laptop webcam still has 720p or 1080 resolutions with low color accuracy and light performance. Understanding the vast market for this NeuralCam introduces an app that converts an apple iPhone into smart webcam. The best part of the deal is its free.

Neuralcam live platform uses machine learning to generate a high-quality computer video stream using the iPhones front camera. Prerequisites are installing the IOS app and MAC driver. iPhone sends a live stream to your computer with features such as video enhancement. Video processing will be handled in the device rather than on the computer. The company is also building an IOS SDK for third-party video calling and streaming apps to control the enhancement features.

The main attractions of the NeuralCam live are

Few shortcomings at present are

A roadmap has been planned by NeuralCam to overcome these drawbacks. They also plan to release windows support soon and serve industries like education, health care, and entertainment.


AI Marketing Strategy Intern: Young inspired management student with a solid background in engineering and technical know-how gathered through work experience with Robert Bosch engineering and business solutions. As a continuous learner, she works towards keeping up-to date with cutting-edge technologies while honing her management and strategy skills to develop a holistic understanding of the modern tech driven industries and drive developments towards better solutions.

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Machine Learning Just Classified Over Half a Million Galaxies – Universe Today

Humanity is still a long way away from a fully artificial intelligence system. For now at least, AI is particularly good at some specialized tasks, such as classifying cats in videos. Now it has a new skill set: identifying spiral patterns in galaxies.

As with all AI skills, this one started out with categorized data. In this case, that data consisted of images of galaxies taken by the Subaru Telescope in Mauna Kea, Hawaii. The telescope is run by the National Astronomical Observatory of Japan (NAOJ), and has identified upwards of 560,000 galaxies in images it has taken.

Only a small sub-set of those half a million were manually categorized by scientists at NAOJ. The scientists then trained a deep-learning algorithm to identify galaxies that contained a spiral pattern, similar to the Milky Way. When applied to a further sub-set of the half a million galaxies (known as a test set), the algorithm accurately classified 97.5% of the galaxies surveyed as either spiral or non-spiral.

The research team then applied the algorithm to the fully 560,000 galaxies identified in the data so far. It classified about 80,000 of them as spiral, leaving about 480,000 as non-spiral galaxies. Admittedly, there may be some galaxies that are actually spirals that were not identified as such by the algorithm, as they might only be visible edge-on from Earths vantage point. In that case, even human classifiers would have a hard time correctly identifying a galaxy as a spiral.

The next step for the researchers is to train the deep learning algorithm to identify even more types and sub-types of galaxies. But to do that, they will need even more well categorized data. To help with that process, they have launched GALAXY CRUISE, a citizen science project where volunteers help to identify galaxies that are merging or colliding. They will be following in the footsteps of another effort by scientists at the Sloan Digital Sky Survey, which used Galaxy Zoo, collection of citizen science projects, to train a AI algorithm to identify spiral vs non-spiral galaxies as well. After the manual classification is done, the team hopes to upgrade the AI algorithm and analyze all half a million galaxies again to see how many of them might be colliding. Who knows, a few of those colliding galaxies might even look like cats.

Learn More:EurekaAlert: Classifying galaxies with artificial intelligencePhysics Letters B: Classifying galaxies with AI and people powerUniverse Today: Try your hand at identifying Astronomers Apply AI to Discover and Classify Galaxies

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How AI & Machine Learning is Infiltrating the Fintech Industry? – Customer Think

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Fintech is a buzzword in the modern world, which essentially means financial technology. It uses technology to offer improved financial services and solutions.

How AI and machine learning are making ways across industries, including fintech? Its an important question in the business world globally.

The use of artificial intelligence (AI) and machine learning (ML) is evolving in the finance market, owing to their exceptional benefits like more efficient processes, better financial analysis and customer engagement.

According to the prediction of Autonomous Research, AI technologies will allow financial institutions to reduce their operational costs by 22%, by 2030.AI and ML are truly efficient tools in the financial sector. In this blog, we are going to discuss how they actually help fintech? What benefits do these technologies can bring to the industry?

The implementation of AI and ML in the financial landscape has been transforming the industry. As fintech is a developing market, it requires industry-specific solutions to meet its goals. AI tools and machine learning can offer something great here.

Are you eager to know the impact of AI and ML on fintech? These disruptive technologies are not only effective in improving the accuracy level but also speeds up the entire financial process by applying various proven methodologies.

AI-based financial solutions are focused on the crucial needs of the modern financial sector such as better customer experience, cost-effectiveness, real-time data integration, and enhanced security. Adoption of AI and allied its applications enables the industry to create a better, engaging financial environment for its customers.

Use of AI and ML has facilitated financial and banking operations. With the help of such smart developments, fintech companies are delivering tailored products and services as per the needs of the evolving market.

According to a study by research group Forrester, around 50% of financial services and insurance companies already use AI globally. And the number is expected to grow with newer technology advancements.

You will be thinking why AI and ML are becoming more important in fintech? In this section, we explain how these technologies are infiltrating the industry.

The need for better, safer, and customized solutions is rising with expectations of customers. Automation has helped the fintech industry to provide better customer service and experience.

Customer-facing systems such as AI interfaces and Chatbots can offer useful advice while reducing the cost of staffing. Moreover, AI can automate the back office process and make it seamless.

Automation can greatly help Fintech firms to save time as well as money. Using AI and ML, the industry has ample opportunities for reducing human errors and improving customer support.

Finance, insurance and banking firms can leverage AI tools to make better decisions. Here management decisions are data-driven, which creates a unique way for management.

Machine learning effectively analyzes the data and brings required outcomes that help officials to cut costs. Also, it empowers organizations to solve specific problems effectively.

Technologies are meant to deliver convenience and improved speed. But, along with these benefits, there is also an increase in online fraud. Keeping this in mind, Fintech companies and financial institutions are investing in AI and machine learning to defeat fraudulent transactions.

AI and machine learning solutions are strong enough to react in real-time and can analyze more data quickly. The organizations can efficiently find patterns and recognize fraudulent process using different models of machine learning. The fintech software development company can help build secured financial software and apps using these technologies.

With AI and ML, a huge amount of data can be analyzed and optimized for better applications. Hence fintech is the right industry where there is a great future of AI and machine learning innovations.

Owing to their potential benefits, automation and machine learning are increasingly used in the Fintech industry. In the case of smart wallets, they learn and monitor users behaviour and activities, so that appropriate information can be provided for their expenses.

Fintech firms are working with development and technology leaders to bring new concepts that are effective and personalized. Artificial intelligence, machine learning, and allied technologies are playing a vital role in financial organizations to improve skills, customer satisfaction, and reduce costs.

In the developing world, it is crucial for fintech companies to categorize clients by data analyzing, and allied patterns. AI tools show excellent capabilities in it to automate the process of profiling clients, based on their risk profile. This profiling work helps experts give product recommendations to customers in an appropriate and automated way.

Predictive analytics is another competitive advantage of using AI tools in the financial sector. It is helpful to improve sales, optimize resource use, and enhance operational efficiency.

With machine learning algorithms, businesses can effectively gather and analyze huge data sets to make faster and more accurate predictions of future trends in the financial market. Accordingly, they can offer specific solutions for customers.

As the market continues to demand easier and faster transactions, emerging technologies, such as artificial intelligence and machine learning, will remain crucial for the Fintech sector.

Innovations based on AI and ML are empowering the Fintech industry significantly. As a result, financial institutions are now offering better financial services to customers with excellence.

Leading financial and banking firms globally are using the convenient features of artificial intelligence to make business more stable and streamlined.

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Informatica Acquires GreenBay Technologies to Advance AI and Machine Learning Capabilities –

REDWOOD CITY, Calif., Aug. 18, 2020 /PRNewswire/ --Informatica, the enterprise cloud data management leader, today announced it has acquired GreenBay Technologies Inc. to accelerate its innovation in AI and machine learning data management technology. The acquisition will strengthen the core capabilities of Informatica's AI-powered CLAIRE engine across its Intelligent Data Platform, empowering businesses to more easily identify, access, and derive insights from organizational data to make informed business decisions.

"We continue to invest and innovate in order to empower enterprises in the shift to the next phase of their digital transformations," said Amit Walia, CEO of Informatica. "GreenBay Technologies is instrumental in delivering on our vision of Data 4.0, by strengthening our ability to deliver AI and machine learning in a cloud-first, cloud-native environment. This acquisition gives us a competitive advantage that will further enable our customers to unleash the power of data to increase productivity with enhanced intelligence and automation."

Core to the GreenBay acquisition are three distinct and advanced capabilities in entity matching, schema matching, and metadata knowledge graphs that will be integrated across Informatica's product portfolio. These technologies will accelerate Informatica's roadmap across Master Data Management, Data Integration, Data Catalog, Data Quality, Data Governance, and Data Privacy.

GreenBay Technologies' AI and machine learning capabilities will be embedded in the CLAIRE engine for a more complete and accurate, 360-degree view and understanding of business, with innovative matching techniques of master data of customers, products, suppliers, and other domains. With the acquisition, GreenBay Technologies will accelerate Informatica's vision for self-integrating systems that automatically infer and link target schemas to source data, enhance capabilities to infer data lineage and relationships, auto-generate and apply data quality rules based on concept schema matching, and increase accuracy of identifying sensitive data across the enterprise data landscape.

GreenBay Technologies was co-founded by Dr. AnHai Doan, University of Wisconsin Madison's Vilas Distinguished Achievement Professor, together with his Ph.D. students, Yash Govind and Derek Paulsen. Dr. Doan oversees multiple data management research projects at the University of Wisconsin's Department of Computer Science and is the co-author of "Principles of Data Integration," a leading textbook in the field, and was among the first to apply machine learning to data integration in 2001. Doan's pioneering work in the area of data integration has received multiple awards, including the prestigious ACM Doctoral Dissertation Award and the Alfred P. Sloan Research Fellowship. Dr. Doan and Informatica have a long history collaborating in the use of AI and machine learning in data management. In 2019, Informatica became the sole investor in GreenBay Technologies, which also has ties to the University of Wisconsin (UW) at Madison and the Wisconsin Alumni Research Foundation (WARF), one of the first and most successful technology transfer offices in the nation focused on advancing transformative discoveries to the marketplace.

"What started as a collaborative project with Informatica's R&D will now help thousands of Informatica customers better manage and utilize their data and solve complex problems at the pace of digital transformation," said Dr. Doan. "GreenBay Technologies will provide Informatica customers with AI and ML innovations for more complete 360 views of the business, self-integrating systems, and more automated data quality and governance tasks."

The GreenBay acquisition is an important part of Informatica's collaboration with academic and research institutions globally to further its vision of AI-powered data management including most recently in Europe with The ADAPT Research Center, a world leader in Natural Language Processing (NLP), in Dublin.

About InformaticaInformatica is the only proven Enterprise Cloud Data Management leader that accelerates data-driven digital transformation. Informatica enables companies to fuel innovation, become more agile, and realize new growth opportunities, resulting in intelligent market disruptions. Over the last 25 years, Informatica has helped more than 9,000 customers unleash the power of data. For more information, call +1 650-385-5000 (1-800-653-3871 in the U.S.), or visit Connect with Informatica on LinkedIn, Twitter, and Facebook.

Informatica and CLAIRE aretrademarks or registered trademarks of Informatica in the United States and in jurisdictions throughout the world. All other company and product names may be trade names or trademarks of their respective owners.

The information provided herein is subject to change without notice. In addition, the development, release, and timing of any product or functionality described today remain at the sole discretion of Informatica and should not be relied upon in making a purchasing decision, nor as a representation, warranty, or commitment to deliver specific products or functionality in the future.

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Machine Learning takes Robotics to the Next Level of Development – Analytics Insight

In the mid-twentieth century when the computer and its applications were starting to bring changes to the world, sociologist David Reisman had something stuck in his mind. He wondered what people would do once machine automation comes to effect and humans have no compulsion to do daily physical chores and strain their brain to come up with solutions. He was excited to see what people would do with all the free time.

More than half a century later when the world has exactly what Reisman wondered, humans are still working on a full-time scale. Work alleviated by industrious machines such as robotics systems has only freed humans to create more elaborate new tasks to be laboured over. To counter attack all the predictions of the previous century, machines gave humans more time to work and not to relax.

Like how we currently imagine robots taking over the human society and doing all the work by themselves including physical and intellectual labour without human assistance as they are well programmed and set to adapt to any environment and take the accurate decision without human help, the previous century people too dreamed that robots will take over all the physical work during the era of the space race. But today, robots are used for their intelligence more vigorously than their physical assistance. Humans can only teach robots and make them follow instructions up to an extent. So when humans lack, machine learning makes its way to discipline robotics.

Machine learningis one of the advanced and innovative technological fields today in which robotics is being influenced. Machine learning aids robots to function with their developed applications and a deep vision.

According to a recentsurveypublished by the Evans Data Corporation Global Development, machine learning and robotics is at the top developers priority for 2016. It is calculated that 56.4% of participants build robotic apps and 24.7% of them indicate the use of machine learning in their project.

Machine learning involves enormous caches of data to be taught to the robot for its perfect learning. The procedure contains algorithms and physical machines to aid the robots in the learning process.

Deep Learning educates the purpose of the robot

Deep Learning has been in the machine learning field for more than 30 years. But it was recognised and brought into continuous use recently when Deep Neutral Network algorithms and hardware advancements started having high potential. Deep learning can be accomplished through computational capacity and the required datasets that are ultimately the powerful assets of machine learning.

The process of teaching robots machine learning necessitates engineers and scientists to decide how AI learns. Domain experts take the next role of advising on how robots need to function and operate within the scope of the task. They also specify the features of robots being of assistance at logistics experts and security consultants. Deep learning focuses on the sector that a robot needs to be specialised from its root.

Feeding robots with planning and learning

AI robotsthrough machine learning acquire two important processes namely planning and learning. Planning is like a physical way of teaching robot that presumes the robots to work on what pace it has to move every joint to complete a task. For example, grabbing an object by a robot is a planning input.

Meanwhile, learning involves different inputs and reacts according to the data added to it on a dynamic environment. Learning process takes place through physical demonstrations in which movements are trained, stimulation of 3D artificial environments and feeding video and data of a person or another robot performing the task it is hoping to master for itself. The stimulation is a training data where a set of labelled or annotated datasets that an AI algorithm can use to recognize and learn from it.

Educating and training with accurate data

The process of educating a robot needs accuracy and abundance. Inaccurate or corrupt data is going to bring nothing except for chaos. Inaccurate data will lead to a robot drawing to the wrong conclusion. For example, if the database is focused on green apples, and you input a picture of a blueberry muffin, they would still get a green apple. This kind of data disruption is a major threat. Insufficient training data will bar the robot from acquiring the full potential it is designed to perform.

Reaping the maximum of physical help

Machine learning will push robots to do physical work at its best. Recently, these kinds of robots are used in industries for various purposes. For example, unmanned vehicles are stealing the spotlight at construction sites.

It is not just the construction sector that is reaping a handful of help from machine learning.Medical industry makes use of itby involving computer vision models to recognise tumours within MRIs and CT scans. Through further training, an AI robot will be capable of doing life-saving surgeries and other medical procedures through its machine learning input.

With the emergence of robots in the society, the opportunity of training data, machine learning and Artificial Intelligence (AI) are playing a critical role in bringing it to enforcement. Tech companies involved in the robot creating and training process should spend some time to sensitize people on the robots help to humanity. If things work well and the AI department comes up with advanced robots that are well-trained, built and purposed AI, Reismans dream of humans having leisure time could come true.

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