Artificial Intelligence Archives - Page 9 of 10

Vaishali
May 21, 2020
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Automated Machine Learning (Automl) | The New Trend In Machine Learning

The digital transformation is driven primarily by the data. So today, companies are searching for as many opportunities to gain as much value from their data as they can. In reality, in recent years, machine learning (ML) has become a fast-growing force across industries. ML ‘s effect on driving software and services in 2017 was immense for companies like Microsoft, Google, and Amazon. And the utility of ML continues to develop in companies of all sizes: examples include fraud prevention, customer service chatbots at banks, automated targeting of consumer segments at marketing agencies, and suggestions for e-commerce goods and retailer personalization. Although ML is a hot subject, there is another popular trend: automated machine learning platform (AutoML). Defining AutoML (Automated Machine Learning) The AutoML field is evolving so rapidly, according to TDWI, there’s no universally agreed-upon definition. Basically, by adding ML to ML itself, AutoML gives expert tools to automate repetitive tasks. The aim of automating ML, according to Google Research, is to build techniques for computers to automatically solve new ML issues, without the need for human ML experts to intercede on each new question. This capability will lead to genuinely smart systems. Furthermore, possibilities are generated thanks to AutoML. These types of technologies, after all, require professional researchers, data scientists and engineers, and worldwide, but such positions are in short supply. Indeed, those positions are so poorly filled that the “citizen data scientist” has arisen. This complementary position, rather than a direct replacement, hires people who lack specialized advanced data scientist expertise. But, using state-of-the-art diagnostic and predictive software, they can produce models. This capability stems from the emergence of AutoML, which can automate many of the tasks that data scientists once perform. To counter the scarcity of AI/ML experts, the AutoML example has the potential to automate some of ML’s most routine activities while improving data scientists’ productivity. Tasks that can be automated include selecting data sources, selecting features, and preparing data, which frees marketing and business analysts time to concentrate on essential tasks. For example, data scientists can fine-tune more new algorithms, create more models in less time, and increase the quality and precision of the model. Automation And Algorithms Organizations have turned toward amplifying the predictive capacity, according to the Harvard Business Review. They’ve combined broad data with complex automated ML to do so. AutoML is marketed as providing opportunities to democratize ML by enabling companies with minimal experience in data science to build analytical pipelines able to solve complex business problems. To illustrate how this works, a current ML pipeline consists of preprocessing, extraction of features, selection of features, engineering of features, selection of algorithms, and tuning of hyper-parameters. But because of the considerable expertise and the time it takes to enforce these measures, there is a high barrier to entry. One of the advantages of AutoML is that it removes some of these constraints by substantially reducing the time it takes to usually execute an ML process under human control, while also increasing the model’s accuracy as opposed to those trained and deployed by humans. Through enacting this, it encourages companies to join ML and free up ML data practitioners and engineers’ resources, allowing them to concentrate on more difficult and challenging challenges. Different Uses Of Automl About 40 percent of data science activities should be automated by 2020, according to Gartner. This automation would result in a broader use by citizen data scientists of data and analytics and improved productivity of skilled data scientists. AutoML tools for this user group typically provide an easy-to-use point-and-click interface for loading ML models for data building. Most Automl tools concentrate on model building rather than automating a whole, particular business feature, such as marketing analytics or customer analytics. However, most Automl tools and ML frameworks do not tackle issues of ongoing data planning, data collection, feature development, and integration of data. It proves to be a problem for people who are data scientists, who have to keep up with large amounts of streaming data and recognize trends that are not apparent. They are still not able to evaluate the streaming data in real-time. And poor business decisions and faulty analytics can arise when the data is not analyzed correctly. Model Building Automation Some businesses have switched to AutoML to automate internal processes, especially building ML models. You may know some of them-Facebook and Google in particular. And Facebook is widely on top of every month’s ML, training, and testing around 300,000 ML models, essentially building an ML assembly line to handle so many models. Asimo is the name of Facebook’s AutoML developer, which produces enhanced versions of existing models automatically. Google also enters the ranks by introducing AutoML techniques to automate the process of discovering optimization models and automating machine learning algorithm design. Automation Of End To End Business Process In certain instances, it is possible to automate entire business processes once the ML models are developed, and a business problem is identified. It needs the data pre-processing and proper function engineering. Zylotech, DataRobot, and Zest Finance are companies that primarily use AutoML for the entire automation of different business processes. Zylotech was developed for the entire customer analytics automation process. The platform features a range of automated ML models with an embedded analytics engine (EAE), automating customer analytics entering the ML process such as convergence, feature development, pattern discovery, data preparation, and model selection. Zylotech allows data scientists and citizen data scientists to access full data almost in real-time, allowing for personalized consumer experiences. DataRobot was developed for predictive analytics automation as a whole. The platform automates the entire lifecycle of modeling, which includes transformations, ingestion of data, and selection of algorithms. The software can be modified, and it can be tailored for particular deployments such as high-volume predictions, and a large number of different models can be created. DataRobot allows citizen data scientists and data scientists to apply predictive analytics algorithms easily and develop models fast. ZestFinance was primarily developed for the

Sulaksh More
April 05, 2020
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Computer Vision in Healthcare – The Epic Transformation

Before discussing futuristic applications of computer vision in healthcare, let us talk a little about how computer vision works. Although the ability to make machines “see” a still image and read it, is related to human’s ability to see, the machines see everything differently. For example, when we see a picture of a car, we see car doors and windows and glasses, color, tires, and background, but what a machine sees is just a series of numbers, that simply describes the technical aspects of the image. Which does not prove that it is a car. Now, to filter out everything and to arrive at a conclusion that it is a car, is what Neural Networks do. Various Neural Networks and Advanced Machine Learning Models are being developed and tested over the period, a massive amount of training data as being fed and machines, now have achieved a level of accuracy. How AI could benefit Health Care Industry: There have been discussions on how AI could help various industries and Health Care is one of the most talked. There are many ways AI could support the industry. AI is a vast field and can be confusing on what specific model to use. There have been continuous discussions and multiple methods approached and improvised. Support Vector Machines For the purpose of classification and regression, Support Vector Machines can be implemented. Here support vectors are data points, which are closest to the hyperplane. To diagnose cancer and other neurological diseases, SVMs are widely used. Natural Language Processing We now have a large amount of data which is composed of examination results, texts, reports, notes and importantly, discharge information. Now, this data could mean nothing for a machine that has no particular training for reading and learning from such data. This is where NLP could be of use, by learning about keywords related to disease and establishing a connection with historical data. NLP might have many more applications based on needs. Neural Networks Implementing hidden layers to identify and establish a connection between input variables and the outcome. The aim is to decrease the average error by estimating the weight between input and output. Image Analysis, Drug Developments and a few, are the fields where Neural Networks are harnessed. As Always, CNNs are the Best: Convolution Neural Networks, over time, has rapidly been developed and currently is one of the most successful computer vision methods. “CNNs simply learns the patterns from the training data set and tries to see such patterns from new images.”. This is the same as humans learning something new and implying the knowledge but what all these models know is a series of ones and zeros. With an accuracy of 95%, a CNN trained at the University of South Florida, can quietly easily detect small lung tumors, often missed by the human eye. Another research paper suggests that cerebral aneurysms can be detected using deep learning algorithms. At Osaka City University Hospital, they detected cerebral aneurysms with 91-93% of sensitivity. RNNs, which are Recurrent Neural Networks are also popular and could be of great use as they are neural networks but with information in sequence. Performing the same task for multiple elements and composing output based on the last computation. How Google’s DeepMind sets new milestones: Acquired by Google in 2014, DeepMind has outplayed many players and has set a new record in AI for the Health care Industry. Protein Folding is something they have been working on and reached a point where predicting the structure of the protein, wholesomely based on its genetic makeup, is possible. What they did was rely on Deep Neural Networks, which are specifically trained to predict protein properties based on the genetic sequence. Finally, they reached a point where they had the model predict the gap between amino acids and the angles connecting the chemical bonds which connect earlier mentioned amino acids. This could also help in understanding the underlying reasons for how genetic mutation results in disease. Whenever the problem with Protein Folding will be solved, it will allow us to pace up our processes like drug discovery, research, and production of such proteins. How could this help in tackling COVID-19 It is not a new discovery that machine learning can fasten the Drug Development Process for any disease or virus. There are very few datasets available related to Corona and has a lot to tackle in order to establish a conclusion. Recently, there have been developments involving AlphaFold, which is a computational chemistry-related deep learning library. FluSense using Raspberry Pi and Neural Computing Engine: Starting with Lab tests, FluSense is now growing to identify and distinguish human coughing from any other sound, in public places. Idea is to combine the coughing data with people present in the area, which might lead to predicting an index of people affected by the flu. This is a perfect use case of computer vision in healthcare considering the recent pandemic of covid-19. Conclusion Though there have been tremendous developments and many new algorithms are been developed, it would be too early to completely rely on a machine’s output. Efficiently detecting minor diseases around the lungs is a great step, but still, a small error could lead to catastrophic events. Few more steps towards better models and we can improve health care, until then we can rely on image analysis systems as an assistant. DataToBiz has been working with a few healthcare startups in shaping up their computer vision products/services. It has been judged time and again as one of the top AI/ML development companies in the industry. Contact our expert and avail of our AI services.

Sulaksh More
March 03, 2020
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AI Edge Computing Technology: Edge Computing and Its Future

After Industrialization in the 20th century, Digitalization is one hot topic and an ever-changing environment. From your smartwatches to Android-powered TVs and wonderful IoT applications. Out of all important aspects for emerging technologies, Data is one of the deciding factors. We now have dedicated teams and departments to utilize the Data, for the purpose of improvement, along with a massive amount of supported computing. What is Edge Computing? Imagine a number of machines, connected internally, sharing data, space and computing, now that’s simply Distributed Computing. Edge Computing, similar to Cloud Computing is built on the same Distributed Computing Architecture but differs largely when it brings Data Storage and Computing handy to the end-user. Edge Computing simply implements decentralization, making sure to abolish the need to send the data back and forth from user to centralized data storage. Processing and analyzing the user data is happening right where data is closest, at the end user. Why does Edge Computing Matters? There are always many reasons why any technology is introduced and implemented. Edge computing enables you to safeguard your sensitive data at the local level, by not sending every data part to centralized data storage. Latency is impressively reduced by not having to make roundtrips to the centralized data storage. Though Cloud and Edge Computing share their Distributed Computing Architecture, edge computing overcomes issues of Latency and bandwidth happening over cloud. Many of the operations happening will largely depend on the hardware capacity of the end-user device instead of centralized data systems. Also increases the chances of reaching out to remote or low network locations. Advantages of Edge Computing To begin with, Edge Computing has a great ability to enrich network performance. Latency in the network has been a major cause for delay and edge computing solves it with its architecture to provide data near the user. From a security perspective, it is a genuine concern that with making the network available to the user, it could be used as an easy entry point for attacks and malware insertions. But the Edge Computing architecture of Distributed Computing prevents such attacks, as it does not transfer data back and forth to the central storage or data center. And it is easier to implement various security protocols at the edge and not compromise the whole network. Most of the data and operations are performed on local devices. The need to establish private centralized data centers for collecting and storing data is a past concern now. With Edge Computing, companies can harness the storage and computing of various connected devices at low cost, resulting in immense computing power. As we understand that edge computing brings the enterprises or the solutions to the end-user, the opposite perspective will be that these large enterprises can easily reach their specific markets on the local level. With local data centers, chances of network crash or shut down are way reduced. With a number of local data centers, most of the problems can be detected and solved at the end-user level and the need to engage centralized systems will be not required. Industries Utilizing Edge Computing With every new technology in the market, many industries have their shares of benefits. Edge computing is set to help Customer Care Industry widely. There has been an impressive attempt to implement artificial Intelligence with customer support and voice assistants like Apple’s Siri and Google Home. Cisco, a company well known for its communication tools has begun experimenting with an edge on their cloud networks. IBM now offers you to combine your edge computing experience with WATSON. Other than that, IBM scientists are working on developing a technology to connect mobile devices without Cellular networks or Wi-Fi. Drones are being used for various purposes and edge technology can be used with drones for functions like visual search and image recognition, object tracking and detection. With AI, drones can be trained to function as human search psychology does in matters of identifying objects and faces. Industries will benefit from more and more computing devices being connected to IoT networks, which will help these industries in reaching wider networks, providing flexibility and reliable services. At DataToBiz, we have built custom digital solutions for businesses in various industries. The AI services that we offer not only help the organizations to scale but also have an ‘edge’ in their market. What could be AI’s role in Edge Computing? What is AI Edge Computing? To put it simply, AI on Edge Computing will have an incredible ability for AI Algorithms to be executed locally, on end-user devices. Most of the AI algorithms are largely based on neural networks which required a massive amount of computing power. Major companies manufacturing Central Processing Units (CPUs), Graphics Processing Units (GPUs) and many higher-end processors have pushed the limits and made AI for edge computing possible. These algorithms will function effectively with local data collected and stored. Another factor will be the requirement of training data for such algorithms, which is a lot smaller for edge computing devices. There have been subtle attempts to implement such AI models on edge computing, which results in impressive benefits to the enterprise as well as to the end-user. To Wrap It Up Edge computing has a wide scope and will be implemented for betterment with end-user as well as enterprise perspective. Along with AI, edge computing will attempt to push the traditional limits of edge and several factors like end-user privacy, data storage, security over usual data transmission, and latency will be improved. Edge Computing as a new approach has uncovered opportunities to implement fresh ways to store and process data. Edge computing has many stored-in answers for many enterprises for multiple problems and will be a real-time efficient solution. We at DataToBiz have been solving a few problems with Jetson Nano, Raspberry Pi, Android Devices & a few other AI edge developer kits. Talk to our AI developer today who will understand your business hurdles and will come up with the ideal solution

Vaishali
March 03, 2020
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What Is Facial Recognition, How It Is Used & What Is It’s Future Scope?

Few biometric innovations cause creativity, like facial recognition. Equally, its launch in 2019 and early 2020 has caused profound doubts and unexpected reactions. But later on, something on that. Within this file, you can uncover the truth and patterns of seven facial detections, expected to change the landscape within 2020. Impact of top innovations and suppliers of AI-Often developing industries in 2019-2024 and leading usage cases Face recognition in China, Asia, the United States, the E.U. and the United Kingdom, Brazil, Russia… Privacy versus security: laissez-faire, enforcement, or prohibition? New hacks: can one trick face recognition? Going forward: the approach is hybridized. How Does Facial Recognition Work? For a human face, the program distinguishes 80 nodal positions. In this case, nodal points are endpoints that are used to calculate a person’s face variables, such as nose length or width, eye socket size, and cheekbone form. The method operates by collecting data on a composite picture of an individual’s face for nodal positions and preserving the resultant data as a faceprint. The faceprint is then used as a reference for contrast with data from faces recorded in a picture or photo. Since the facial recognition technology requires just 80 nodal points, when the circumstances are optimal, it can quickly and reliably recognize target individuals. Nonetheless, this form of algorithm is less effective if the subject’s face is partly blurred or in shadow rather than looking forward. The frequency of false positives in facial recognition systems has been halved every two years since 1993, according to the National Institute of Standards and Technology (NIST). High-quality cameras in mobile devices have rendered facial recognition both a viable authentication and identification choice. For example, Apple’s iPhone X and Xs include Face ID technology, which enables users to unlock their phones with a faceprint mapped by the camera on the phone. The phone’s program, which is designed to avoid being spoofed by images or masks utilizing 3-D mapping, records, and contrasts over 30,000 variables. Face ID can be used to authenticate transactions in the iTunes Store, App Store, and iBookStore via Apple Pay and. Apple encrypts and saves cloud-based faceprint data, but authentication takes place directly on the computer. Smart airport ads will now recognize a passer-by’s gender, race, and estimated age and tailor the advertising to the profile of the user. Facebook utilizes face recognition tools for marking images of people. When an individual is marked on an image, the program stores mapping details regarding the facial features of that individual, once appropriate data has been obtained, and the algorithm may use the information to recognize the face of a single person as it occurs in a new picture. To preserve the privacy of users, a function named Picture Check notifies the designated Facebook user. Many forms of facial recognition include eBay, MasterCard, and Alibaba, which, usually referred to as selfie pay, have carried out facial recognition payment methods. The Google Arts & Culture software utilizes facial detection to recognize doppelgangers in museums by comparing the faceprint of a live individual with the faceprint of a portrait. Step 1: The camera can identify and remember one object, either alone or in a crowd, to begin. The face is easily recognized while the individual is staring at the camera directly. The scientific advancements have often rendered it more comfortable to figure out minor differences from this. Step 2: First, they take and examine a snapshot of the nose. Some face recognition is based on 2D photos rather than 3D since it will more easily align a 2D object with the public or archive photographs. Each face is comprised of distinctive landmarks or nodal points. Each human face has 80 nodal dots. Technology for facial recognition analyzes nodal points such as the distance between the eyes or the outline of your cheekbones. Step 3: Your facial examination is then translated into a statistical model. Such facial features are numbers in a database. This file numeric is considered a faceprint. Every individual has his faceprint, similar to the specific structure of a thumbprint. Step 4: Your code is then matched to another faceprint database. This website has images that can be paired with identifiers. More than 641 567million files are open to the FBI through 21 state repositories such as DMVs. Facebook’s images are another illustration of a website millions had exposure. All images which are marked with the name of an individual are part of the Facebook archive. The code instead finds a fit in the supplied database with the exact apps. This returns with the match and details, including name and address added. Developers will use Amazon Recognition, an image processing tool that is part of the Amazon A.I. series, to attach functionality for face recognition and interpretation to a device. Google has a similar functionality through its Google Cloud Vision API. The platform used to track, pair, and classify faces through machine learning is utilized in a broad range of areas, including entertainment and marketing. For starters, the Kinect motion game device makes use of facial recognition to differentiate between participants. Uses of Facial Recognition You Must Know! Face detection may be used for a broad range of purposes, from defense to ads. Any examples in usage include Smartphone makers, including Apple, for public protection. S. Government at airports, by the Homeland Security Agency, to recognize people who can meet their visa criteria. Law enforcement by gathering mugshots can evaluate local, national, and federal assets repositories too. Social networking is used for identifying individuals in photos, which also includes Twitter. Business protection, as businesses may use facial recognition to access their buildings. Marketing, where advertisers may use facial recognition to assess particular age, gender, and ethnicity A variety of possible advantages come with the usage of facial recognition. There is no need to directly touch an authentication system relative to other touch-based biometric identification methods such as fingerprint scanners, which could not function well if a person’s hand is soil. The safety standard

Vaishali
February 20, 2020
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Everything You Need to Know About Computer Vision

To most, they consist of pixels only, but digital images, like any other form of content, can be mined for data by computers. Further, they can also be analyzed afterward. Use image processing methods, including computers, to retrieve the information from still photographs, and even videos. Here we are going to discuss everything you must know about computer vision. There are two forms-Machine Vision, which is this tech’s more “traditional” type, and Computer Vision (CV), a digital world offshoot. While the first is mostly for industrial use, as an example are cameras on a conveyor belt in an industrial plant, the second is to teach computers to extract and understand “hidden” data inside digital images and videos. Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to the detection and labeling of objects has been able to surpass humans. One of the driving factors behind computer vision development is the amount of data we produce now, which will then get used to educate and develop computer vision. What is Computer Vision? Computer vision is a field of computer science that develops techniques and systems to help computers ‘see’ and ‘read’ digital images like the human mind does. The idea of computer vision is to train computers to understand and analyze an image at the pixel level. Images are found in abundance on the internet and in our smartphones, laptops, etc. We take pictures and share them on social media, and upload videos to platforms like YouTube, etc. All these constitute data and are used by various businesses for business/ consumer analytics. However, searching for relevant information in visual format hasn’t been an easy task. The algorithms had to rely on meta descriptions to ‘know’ what the image or video represented. It means that useful information could be lost if the meta description wasn’t updated or didn’t match the search terms. Computer vision is the answer to this problem. The system can now read the image and see if it is relevant to the search. CV empowers systems to describe and recognize an image/ video the way a person can identify a picture they saw earlier. Computer vision is a branch of artificial intelligence where the algorithms are trained to understand and analyze images to make decisions. It is the process of automating human insights in computers. Computer Vision helps empower businesses with the following: Computer vision is largely being used in hospitals to assist doctors in identifying diseased cells and highlighting the probability of a patient contracting the disease in the near future. Computer vision is a field of artificial intelligence and machine learning. It is a multidisciplinary field of study used for image analysis and pattern recognition. Emerging Computer Vision Trends in 2022 Following are some of the emerging trends in computer vision and data analytics: One of the most vigorous and convincing forms of AI is machine vision that you’ve almost definitely seen without even understanding in any number of ways. Here’s a rundown of what it’s like, how it functions, and why it’s so amazing (and will only get better). Computer vision is the computer science area that focuses on the replication of the parts of the complexity of the human visual system as well as enables computers to recognize and process objects in images and videos in the same manner as humans do. Computer vision had only operated in a limited capacity until recently. Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to the detection and labeling of objects has been able to surpass humans. One of the driving factors behind computer vision growth is the amount of data we generate today, which will then get used to train and improve computer vision. In addition to a tremendous amount of visual data (more than 3 billion photographs get exchanged daily online), the computing power needed to analyze the data is now accessible. As the area of computer vision has expanded with new hardware and algorithms, the performance ratings for the recognition of artifacts also have. Today’s devices have achieved 99 percent precision from 50 percent in less than a decade, rendering them more effective than humans in reacting quickly to visual inputs. Early computer vision research started in the 1950s, and by the 1970s it was first put to practical use to differentiate between typed and handwritten text, today, computer vision implementations have grown exponentially. How does Computer Vision Work? One of the big open questions in both neuroscience and machine learning is: Why precisely are our brains functioning, and how can we infer it with our algorithms? The irony is that there are very few practical and systematic brain computing theories. Therefore, even though the fact that Neural Nets are meant to “imitate the way the brain functions,” no one is quite positive if that is valid. The same problem holds with computer vision— because we’re not sure how the brain and eyes interpret things, it’s hard to say how well the techniques used in development mimic our internal mental method. Computer vision is all about pattern recognition on an individual level. Also, one way is to train a machine on how to interpret visual data is to feed. It can get supplied with pictures, hundreds of thousands of images, if possible millions that have got labeled. Also, later on, they can be exposed to different software techniques or algorithms. Further, these can enable the computer to find patterns in all the elements that contribute to those labels. For example, if you feed a computer with a million images of cats (we all love them), it will subject them all to algorithms. Further, that will allow them to analyze the colors in the photo, the shapes, the distances between

Parindsheel Dhillon
February 08, 2020
1 Comment

Outsourcing AI Requirement To AI Companies Is a New Emergent Trend: An Analysis Justifying It

Are you thinking of outsourcing AI requirement, when you are not sure of the value it can add to your business, during its initial phase of R&D. Whether it is the e-Commerce retail giants Amazon, eBay or an emerging startup, they all have one thing in common, the acceptance to technological advancements and the willingness to adopt it in their process automation. In their visions, AI’s role has been crucial. On a larger scale, Amazon has been automating its godown and warehouses with RPAs or (Robotic Process Automation) by signing a deal with Kiva Systems, a Massachusetts based startup that has been working on making AI robots and software. The report from PwC, a professional service network specify that nearly 45% of the current work is automated in many organizations. Such an approach leads to an annual $2 trillion in savings. Even the emerging startups have started to integrate chatbots in their process management for simplifying the customer engagement process. All these businesses have focussed on outsourcing their AI needs to other companies having the domain expertise in AI. Therefore, it is evident that such a trend has been persistent and will sustain for long in the near future. Let’s look at why this trend is becoming mainstream and why it is beneficial for companies to outsource their AI requirements to other domain experts. Benefits Companies Receive When They Outsource Their AI Access to Top Level Resources or also known as Connoisseurs in AI Companies/corporations work at different wavelengths, and domain expertise differs for all. For example, a company in the retail, supply chain, or logistics might not be an expert in technology. But they do need smart technological solutions that can automate tasks, eliminate the need for workers for menial jobs, and ways that can cut down the operational budget. Though they have full knowledge of their process and domain, having experts to sit in-house for programming, development, and deployment will cost them fortunes. When these companies outsource to AI-oriented companies with expertise in Robotic Process Automation, Business Intelligence, Data Mining, and Visualization, it helps them save additional expenditure from setting up a new tech process and face mental hassles to manage the same. As a result, top companies, whether SMEs, startups, or even MNCs prefer to outsource their AI needs to domain experts in the market. On-Time Delivery of Services & Products On-time delivery is a pressing challenge when you have an in-house team to manage the development, testing and delivery process. For example, a retail giant like Amazon or eBay is more interested in improvising their delivery system, product quality and price optimization rather than spending time manufacturing robots or managing data of consumers on their own. At such instance, they need the support of data management and manufacturing companies on the AI domain to help create feasible solutions for them. Having an expert AI company can assure them of on-time delivery without compromising on the quality. The result would be satisfied and happy customers for the companies hiring AI service provider for their niche based requirement. Setting Up Smooth Business Process Smooth business process using AI solution works best when you have the customized solution provider in the market working on solving your challenges. Most AI driven applications need prevailing market analytics and trends to be incorporated for better performance. Companies who decide to build and manage their AI applications on their own if they excel in different sectors won’t meet the desired results when compared to AI oriented solution providers. Those companies whose main product is AI solutions are continuously monitoring trends and upgrades. They partner with numerous AI based companies, volunteer in AI workshops and programs to further enrich their knowledge base. Thus, ending up as best for companies who want to integrate AI solutions in their scheme of work. These AI based startups, or established companies understand the process of their clients and customize the product to best fit into their requirements. For example, Apple’s Siri, or NetFlix customized content shown to users are best use cases to show how AI can simplify the user experience and set-up a smooth process as per the changing needs of the business. But for banks, pharmaceutical or logistics sectors to develop their own solutions like Apple’s Siri or Netflix’s customized AI data analytics would be a tough job to achieve. Even if they do invest into it, the time investment required to keep things in order might disrupt their natural business process. Hence, they find it much more feasible and cost effective to outsource it to an AI company and develop the solutions on their behalf. Save Expenses In A Big Way For sustainability, businesses have to understand the challenges, market dynamics and adapt to the changes every now and then. Such an approach requires a lot of time investment and spending time to create AI based solutions to simplify their process will be an added liability for resource and time. When companies in other sectors outsource their AI based requirements to a technology company excelling in AI, they save the time and cost. As a result, most companies are willing to outsource their requirements to a tech company rather than managing on their own. Conclusion Outsourcing to AI companies help build customized solution and they bring a lot of advantages for businesses who want to resolve their challenges in the most cost effective manner. When you analyze and find out that even top giants like Amazon and Apple are willing to outsource their specific process to AI companies, it wouldn’t be wrong to conclude that outsourcing looks much more feasible option for most companies these days. We at DataToBiz help our partners in their initial phases of R&D involving Artificial technologies. Contact for further details

Vaishali
January 28, 2020
1 Comment

10 Amazing Advantages of Machine Learning You Should Be Aware Of!

Machine learning (ML) extracts concrete lessons from raw data to solve complex, data-rich business problems fast. ML algorithms iteratively learn from the data and enable computers to discover various types of deep insights without being specially trained to do so. ML develops at such a rapid rate and is driven primarily by emerging computational technology. Machine learning in business helps improve business scalability and business operations for companies around the globe. In the business analytics community, artificial intelligence tools and numerous ML algorithms have gained tremendous popularity. Factors including rising quantities, convenient data access, simpler and quicker computer capacity, and inexpensive data storage have led to a massive boom in machine learning. Organizations can, therefore, profit from knowing how businesses can use machine learning to apply the same in their processes. Machine learning (ML) and Artificial Intelligence in the business sector have created a lot of hype. Marketers and business analysts are curious to learn about the advantages of machine learning in the industry and its implementations. For ML architectures and Artificial Intelligence, several people have heard for. But they’re not entirely conscious of it and its implementations. You must be mindful of the business problems it can address to use the ML in the market. Machine learning collects useful raw data knowledge and offers detailed tests. And that knowledge helps to solve dynamic and data-rich issues. Machine learning algorithms, too, learn and process from the input. The methodology is used without needing to be trained to find different perspectives. The ML is rapidly evolving and being powered by new technologies. It also allows the company to boost regional organization scalability and business operations. Recently, in their company, several top-ranking businesses such as Google, Amazon, and Microsoft have embraced machine learning. And they’ve introduced tools for online machine learning. Why Machine Learning Is Important? Machine learning is important because it primarily works with a huge variety of data. Processing big data is cheaper when you use an algorithm to automate the process rather than rely on manual processes done by humans. A machine learning algorithm can be quickly trained to analyze datasets and detect patterns that are not easily identifiable otherwise. ML makes automation possible, which, in turn, saves time, money, and resources for an enterprise. When you can get better and more accurate results for a fraction of a cost and in a handful of minutes, why not invest in machine learning models? Here’s why machine learning is important in today’s world: Voice assistants use Natural Language Processing (NLP) to recognize speech and convert it into numbers using machine learning. The voice assistant then responds appropriately. While Google Assistant, Siri, etc., are used in domestic life, organizations are using similar voice assistants at the workplace to help employees interact with machines using their voices. It promotes self-service and allows employees to rely on technology instead of their colleagues to finish a task. Companies in the transportation industry (like Ola, Uber, etc.) use machine learning to optimize their transportation services. Planning the best route, setting up dynamic pricing based on the traffic conditions, and other such aspects are managed using machine learning software. ML also helps create better physical security systems to detect intruders, prevent fake alarms, and manage human screening in large gatherings Machine learning helps improve the quality of output by minimizing/ preventing bottlenecks. Be it the production lifecycle, cyber security, fraud detection, risk mitigation, or data analytics, ML technology offers valuable insights in real-time and gives businesses an edge over the competitors. Some Basic Advantages of Machine Learning Here are some of the major benefits of machine learning that every businessman must be aware of. Each business organization relies on the information received through data analysis. Big data is on the businesses. But it’s difficult to extract the right information and make a decision from the results. Machine learning takes advantage of ML algorithms. It also learns from data already in use. The findings help to make the right decision for the businesses. It allows companies to turn data into knowledge and intelligence that can be used. The experience will work into daily business processes. These processes then deal with changes in market requirements and business circumstances. Business organizations should use machine learning in this way. It holds them on top of the rivals. Top Advantages Of Machine Learning ML aims to derive meaningful information from an immense amount of raw data. If implemented correctly, ML can act as a remedy to a variety of problems of market challenges and anticipate complicated consumer behaviors. We’ve already seen some of the significant technology companies coming up with their Cloud Machine Learning solutions, such as Google, Amazon, Microsoft, etc. Here are some of the critical ways ML can support your company: 1. Customer Lifetime Value Prediction Prediction of consumer lifetime value and segmentation of consumers are some of the significant challenges the advertisers face today. The business has exposure to vast amounts of data, which can be used easily to provide insightful insights into the Market. ML and data mining will help companies to forecast consumer habits, purchasing trends, and improve individual customers to submit best-possible deals based on their surfing and purchase experience. 2. Predictive Maintenance Manufacturing companies regularly follow patterns of preventive and corrective repair, which are often costly and ineffective. With the emergence of ML, though, businesses in this field will make use of ML to uncover valuable observations and trends embedded in their data on their factories. It is recognized as predictive maintenance, which helps reduce the risks of unforeseen problems and reduces needless expenditures. Historical data, workflow visualization tools, flexible analytical environments, and feedback loops can be used to build ML architecture. 3. Eliminates Manual Data Entry Duplicate and unreliable records are among the most significant problems. The businesses are facing today. Machine Learning algorithms and predictive models will significantly prevent any errors induced by manual data entry. ML programs use the discovered data to make these processes better. The employees can, therefore,

Kavika Roy
August 27, 2018
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AI in Pharma: How Pharma Industry is Getting Smarter Today

Artificial Intelligence or AI in the pharma industry presents various opportunities to substantially improve the pace of drug discovery and distribution process. The current protocol followed needs to be upgraded in order to meet the rising demand for medicine and that too without compromising its quality. Advanced AI solutions will help pharma companies to process structured and unstructured data in order to derive useful and actionable insights. The application of machine learning and AI to drug discovery will not only accelerate the process but also help companies to spawn a higher return on investment. It will make it easier for scientists to find potential targets and for the manufacturers to ensure its timely delivery. McKinsey estimates that machine learning and big data can help to generate a profit of around $ 100 billion for the pharma industry. The insights produced with the help of analytics would help the pharma companies to make better decisions, improve the efficiency of clinical trials, advance the shipping process and ultimately achieve greater commercial success. What is Artificial Intelligence in Pharmaceutical Industry? AI in the pharma industry is the use of algorithms, computer vision technologies, and automation to speed up tasks that were traditionally performed by humans. The pharma and biotech industry saw huge investments in artificial intelligence in recent times. From market research to drug development and cost management, AI is playing a vital role in modernizing the pharma industry and bringing new drugs faster into the market. Big data and AI-based advanced analytics have brought a radical change in the pharma sector. Faster innovation, increase in productivity, and building comprehensive supply chain systems are possible with artificial intelligence. According to a study conducted by the Massachusetts Institute of Technology (MIT), less than 14% of the new drugs pass clinical trials. Moreover, the pharma company has to pay billions to get the drug approved by the government authorities. By using artificial intelligence in pharmaceutical research and development, pharma companies can increase their success rate. The data from clinical trials are collected and processed using AI and ML systems to derive insights about the drug and its reactions to the test subjects. The positives and side effects are carefully observed and analyzed to make the necessary changes to the drug’s composition. This will result in drugs with a better curing capacity and fewer side effects. The pharma industry requires billions to keep up the R&D. The company spends huge amounts of money at every stage to ensure that the drug is made using quality materials and in hygienic and sterile conditions. The warehouse for storing inventory should have a temperature control facility to maintain the necessary conditions for the drugs to retain their original composition. By adopting artificial intelligence software apps and integrating them with systems in the pharma company, the management can streamline the process from start to finish. This will reduce operational costs and minimize the risk of damaging the drugs. Let’s take Novartis as an example. The pharma company is investing in AI and ML to find ways to speed up the treatment processes and help patients become healthier. The company is working on classifying digital images of cells based on how they are responding to treatment (compounds). The ML algorithms collect the research data and group cells with similar responses to the compounds used for the treatment. This information is then shared with the research team to help them use the insights and their experience in understanding the results. Novartis uses the images developed by machine learning algorithms to run predictive analytics and identify cells that may not respond to the treatment. The ML algorithms make it easier to study large amounts of data and identify the patterns of different diseases, their impact on the cells and organs, the symptoms, and the possible treatment methods/ drugs that can cure the diseases. A pharma company that invests in adopting artificial intelligence at each level (R&D, production, supply chain, etc.) will have an edge over competitors and can provide expensive drugs for cost-effective prices to make treatment affordable for more patients. AI in Pharma Industry: The Transformation Look how ML and AI models are transforming the pharma industry and making it even better than before. Supply Chain Management Optimization of the supply chain across pharmaceutical industries has always been a challenge for the owners. However, with the advent of AI and ML, the process is becoming smoother. The big data generated helps companies to reach out to their prospective clients and understand their needs, which in turn ensures the number of drugs to be produced by the companies. Also, predictive analytics insights generated with the help of big data allow the companies to foresee the demand pattern and hence manufacture only the required quantity of medicines. The drugs today are being increasingly customized for even small populations with particular genetic profiles. Finding out a way to deliver a medicine that is relevant only to a small bunch of 1000 people is more difficult than delivering medicines across the world. This venture requires proper utilization of resources so that there is no delay in delivery and loss to the company. An expert at “LogiPharmaUS Conference” in 2017 said that “Instead of executing one supply chain a thousand times, we should get ready to execute a thousand supply chains, one at a time.” This act will not only ensure timely drug delivery but also safeguard the hassle of re-execution every time. Machine learning and AI algorithms can help to automate this process and make it more robust. Now, when we talk only about shipping drugs, there are many medicines that are expensive and require very specific conditions to be transported. Billions and trillions of money are spent by the pharma companies to deal with the transportation process. With the application of ML and AI pharma, companies will be able to forecast demand and distribute products efficiently. Also, many key decisions will become automated allowing the companies to cut down their labor costs and make more profit.

Kavika Roy
July 17, 2018
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11 Insane Machine Learning Myths Debunked for You!

The world is becoming smart, smarter than ever before. There are homes that know how to turn on the lights by judging their intensity and there are cars that can drive themselves. Isn’t it something like living in a sci-fi world? Everything that was imagined is turning into reality. Among all that we hear about the upcoming technology, machine learning (ML) is a common term being associated with almost all of them. The term has been more misinterpreted than understood and there has been a considerable measure of hype buzzing around it. With more gadgets and technologies being launched every day, customers are keen to know what is it that is making them smarter? They are curious to discern the tech running behind the smartness and understand how it can benefit them in their personal as well as business ventures. This inquisitiveness towards the “working” has lured people to read and question about the same, however, the responses have not been palatable. For instance, you may often see mobile companies using the terms artificial intelligence and machine learning interchangeably for their products, now this is how a misperception is shaped. The customers do not understand the difference between the two and start treating them as synonymous with each other. The aim here is to make you understand the similarities and differences between “machine learning” and the terms it is confused with. this write-up shall provide you with a clear insight so that you can differentiate between the hype and the reality. It is important because machine learning forms an integral part of almost all data-driven work. In the event that you intend to consolidate it into your business, you should discern what it may or may not be able to do for you. Having a clear perspective will ensure that you develop a strategy that fits into your business module and helps you accomplish the set objectives. Removing the Misconception You know how they say in school that if your basics are clear, you will understand each and every concept and if not then surely there will be trouble. This concept will hold true in your entire life and therefore if you recognize the simple notion of machine learning you’ll never be influenced by the related hysterias. The figure below describes machine learning in its most naive form. There is a lot of reality and there is a lot of hype pertaining to machine learning. But with the above-illustrated diagram, it should be clear that machine learning is, training a machine by giving it a large amount of data and then letting it perform based on that learning. Exposing the Machine Learning Myths Machine learning is currently going through a phase of inflated expectations. Along with ongoing machine learning developments around the globe , there are still a lot of organizations looking forward to conceptualizing and running ML projects without even exploring the power of basic analytics. How do you expect them to meet their goals when they do not know what ML can or cannot do? In such a scenario it becomes imperative to know the myths and truths related to the subject. #1 Machine Learning and Artificial Intelligence Are Same One of the most common misconceptions is between artificial intelligence and machine learning. Both the terms are not only different in words but are two different fields belonging to a bigger pool of data science. In order to understand the difference consider this example – You wish that the camera of your phone should recognize a dog. Now in order to do that you provide it with a huge amount of data that contains pictures of all the types of dogs present in the world. With the help of these images, the camera is able to create a pattern that resembles a dog. Now whenever you point the camera toward the dog, it matches the pattern and that is how you get a positive hit. On the other hand, pointing the camera toward a cat doesn’t identify it as anything. This is a machine-learning process where the machine is trained to accomplish a particular task. Artificial Intelligence on the other hand is a broader concept, where the machines are trained in such a way that they can make their own decisions just like the human brain. If you put a cat in front of a camera that works on an AI technology, it will use it as another input and further reuse it to train itself. This training would help the AI-enabled phone to tell that isn’t a dog but it may be something else that can be explored. #2 Hiring the Best ML Talent Is Sufficient to Resolve Business Issues Business firms are spending a lot of money in gathering the best machine learning talent which can analyze their data and offer useful insights. What they forget in the process is that machine learning is just one part of an effective strategy, the basics are to have the right type and amount of data. If there is no one who can fetch the data, what will the professionals work upon? Therefore, businesses do not need a staff good in one field but someone who knows how to work from the scratch. There are data science firms all over the globe that can help businesses develop a correct approach and provide the useful insights they have been looking for. #3 ML Implementation Requires Humongous Infrastructure Machine learning sounds scientific and complicated that many presume it is not meant for their business. After all, what will an ordinary business do with advanced technology? Not every SME hires AI experts, isn’t it? That’s where we are wrong Years before it was said that if you wish to carry our ML operations on your premises, you’ll need to invest a large amount in infrastructure. The scenarios have changed now. Since data science and data analytics has become such an integral part of the business world, there are professionals who are

Parindsheel Dhillon
November 18, 2017
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6 Innovative Ways of Using Machine Learning in E-Commerce

Machine learning is one of the most searched keyword on any search engine at this point of time. The reason is quite clear; the benefits of utilising it in any industry is beyond imagination. We are explaining how an e-commerce business can make use of machine learning for profit maximisation