In the early days, the education setup was about a tree, chalk, and slate with a change of time, restored as a luxury space that had everything to do with human effort in terms of learning.Today, most of all, the whole focus is shifting towards innovation, creativity, and technological advancement.Previously restricted to companies and laboratories, it is now a boon for young adults to effectively nourish their minds without technical side effects.Educational robotics is a useful tool in early and special education.Social and personal skills can be developed through educational robotics.Impact on Formal Education:This discovery has had a major impact on the formal education system of the country.On the other hand, this has led to less mental exercise of the human mind, which is considered essential for full growth.On the bright side, this allows a person to learn faster and keep up with the rapid growth rate of innovation.This type of learning has proven to make technology and programming more enjoyable.
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AI has been the latest trend which is implemented across various sectors.Its subsets Machine Learning & Deep Learning are also making huge impacts.In this blog, we have discussed in detail about them.Learn More at :Digital Transfomation Companies in Chennai. 
Machine learning could become a new weapon in the fight against Medicare fraud.Machine learning can be a useful tool in detecting Medicare fraud, according to a new study that can recover anywhere from $ 19 billion to $ 65 billion lost in fraud each year.Researchers at Florida Atlantic University’s College of Engineering and Computer Science recently published the world’s first study using Medicare Big data, machine learning, and advanced analytics to automate fraud detection.They tested six different machine learners on balanced and unbalanced data sets and eventually found that the RF100 Random Forest algorithm would be most effective in detecting potential cases of fraud.They found that unbalanced data sets are more than balanced data sets when scanning for fraud.There are many implications in determining what fraud is and what is not, such as clerical error,” says Richard A. Bowder, senior author and Ph.D.“Our goal is to allow machine learners to know all this data and flag anything suspicious.Then we can alert researchers and auditors, who should focus on 50 cases instead of 500 cases or more.”In the study, Bowder and colleagues examined Medicare data, covering 37 million cases from 2012 to 2015, for incidents such as patient abuse, neglect, and billing for medical services.The team has reduced the data set to 3.7 million cases, which is still a challenge for human researchers charged with pinpointing Medicare fraud.The authors used the National Provider Identifier — a government-issued ID number for health care providers to compare fraud labels with Medicare Part data, which includes provider details, payment and charge information, policy codes, all policies, and medical specifications.When researchers compared NPI with Medicare data, they flagged fraudulent providers in a separate database.“If we can accurately assess the physician’s uniqueness based on our statistical analyses, then we can detect exceptional physician behaviors and flag as much fraud as possible for further investigation,” said Tagi M. Khoshgofthar, Ph.D., co-author, and professor at the school.So, if a cardiologist is wrongly labeled a neurologist, it is a sign of deception.However, the data set remains a challenge.A small number of fraudulent providers and a large number of onboard providers have made data imbalance that can fool machine practitioners.
You are looking at the latest home security technologies so that you can protect yourself from the threat.Security systems of the past have used common sensors and alarms to detect intruders, and they consult a team of security experts in case something goes wrong.Many people have returned from holiday or business meetings, knowing that this is only a false alarm.Your home will notify you when the move or the door is open and watching the live feed will allow you to determine if you are facing a real threat or a false alarm.Some police departments have reported that 90 percent of alarms are false.Giving you the power to disable the alarm when there is no threat will save you time and money, but if you do not respond quickly, the software will contact the authorities, keeping you out of harm’s way.Facial recognitionWhile past security systems can always detect weapons and movements when their alarm is heard, facial recognition can reduce the number of false alarms you experience with AI.The cameras of your security system can detect your face and the faces of the people you invite to your home on a regular basis.If your alarm and your children or spouse come home, your security system will not contact the police until they realize they are allowed in your home.On the other hand, if the face is not allowed in front of you, it will send help.
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Unfortunately, managers often lack understanding when it comes to AI and it started with the term itself.Computers are given the opportunity to learn without explicit programming.It helps especially when it comes to recognizing patterns and classification.Natural language processingNLP is concerned with interactions between human languages and computers.This is an underrated, potentially transformative technology with many applications such as automating business intelligence reports, product descriptions, financial reports, meeting memos, and more.The ability to create custom, ad hoc content with the incidental incremental cost is more powerful than many realize.Other players to watch in this market include Lucidworks, Attivio, SAS, Narrative Science, Digital Reasoning, Yseop, and Cambridge Semantics.5.
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At work, chatbots are affiliated with the Customer Support Team, with estimates that they will be responsible for 85% of customer service by next year.There are also intelligent algorithms that can use a lot of data to make accurate predictive behavior of people and clients.However, although these concepts are all linked, they are not the same thing.As intelligence experts explain, different parts of AI are positioned as Russian nesting dolls.Artificial intelligence is “smart” because it can follow very complex instructions without responding to a single or basic trigger.In recent years, AI has gained in popularity, thanks to the increase in available GPUs that make parallel processing easier, cheaper, and more accessible.With machine learning tools, it is possible to establish computer algorithms that are searchable by data and apply heaps of knowledge and training to a specific task.For example, machine learning service can use millions of face images to identify specific people or certain features on the face.The artificial intelligence we have today falls into the categories of narrow AI and artificial general intelligence.Narrow AI is a “weak” AI that works in a limited context.So, how does machine learning work?Machine learning uses two basic methods to deliver results.
Every day, new examples are coming out of new problems being solved and old markets being disrupted by what is collectively called “Artificial Intelligence.” Enterprises that do not have an AI strategy would be wise to start working on one straight away.Unfortunately, managers often lack understanding when it comes to AI and it started with the term itself.Computers are given the opportunity to learn without explicit programming.It helps especially when it comes to recognizing patterns and classification.Natural language processingNLP is concerned with interactions between human languages and computers.This is an underrated, potentially transformative technology with many applications such as automating business intelligence reports, product descriptions, financial reports, meeting memos, and more.The ability to create custom, ad hoc content with the incidental incremental cost is more powerful than many realize.Other players to watch in this market include Lucidworks, Attivio, SAS, Narrative Science, Digital Reasoning, Yseop, and Cambridge Semantics.5.
That may seem like a cliché, or hype, or buzz, but it is true.The tech is fundamentally changing the way packages move around the world, from predictive analytics to autonomous vehicles and robotics.Here are the top five ways in which Artificial Intelligence is transforming the logistics industry as we know it:Predictive Capabilities Skyrocket When AI in Logistics is ImplementedThe capabilities of AI are seriously ramping up company efficiencies in the areas of predictive demand and network planning.Having a tool for accurate demand forecasting and capacity planning allows companies to be more proactive.By knowing what to expect, they can decrease the number of total vehicles needed for transport and direct them to the locations where the demand is expected, which leads to significantly lower operational costs.The tech is using data to its full potential to better anticipate events, avoid risks, and create solutions.This allows organizations to then modify how resources are used for maximum benefit — and Artificial Intelligence can do these equations much faster and more accurately than ever before.In general, predictive analytics solutions in the logistics and supply chains are on the rise.The most well-known examples are Transmetrics and ClearMetal, which were both mentioned in the latest DHL’s Logistics Trend Radar.AI analysis can also be used to safeguard against risk.Another good example from DHL is its platform which monitors more than 8 million online and social media posts to identify potential supply chain problems.
Machine learning could become a new weapon in the fight against Medicare fraud.Machine learning can be a useful tool in detecting Medicare fraud, according to a new study that can recover anywhere from $ 19 billion to $ 65 billion lost in fraud each year.Researchers at Florida Atlantic University’s College of Engineering and Computer Science recently published the world’s first study using Medicare Big data, machine learning, and advanced analytics to automate fraud detection.They tested six different machine learners on balanced and unbalanced data sets and eventually found that the RF100 Random Forest algorithm would be most effective in detecting potential cases of fraud.They found that unbalanced data sets are more than balanced data sets when scanning for fraud.There are many implications in determining what fraud is and what is not, such as clerical error,” says Richard A. Bowder, senior author and Ph.D.“Our goal is to allow machine learners to know all this data and flag anything suspicious.Then we can alert researchers and auditors, who should focus on 50 cases instead of 500 cases or more.”In the study, Bowder and colleagues examined Medicare data, covering 37 million cases from 2012 to 2015, for incidents such as patient abuse, neglect, and billing for medical services.The team has reduced the data set to 3.7 million cases, which is still a challenge for human researchers charged with pinpointing Medicare fraud.The authors used the National Provider Identifier — a government-issued ID number for health care providers to compare fraud labels with Medicare Part data, which includes provider details, payment and charge information, policy codes, all policies, and medical specifications.When researchers compared NPI with Medicare data, they flagged fraudulent providers in a separate database.“If we can accurately assess the physician’s uniqueness based on our statistical analyses, then we can detect exceptional physician behaviors and flag as much fraud as possible for further investigation,” said Tagi M. Khoshgofthar, Ph.D., co-author, and professor at the school.So, if a cardiologist is wrongly labeled a neurologist, it is a sign of deception.However, the data set remains a challenge.A small number of fraudulent providers and a large number of onboard providers have made data imbalance that can fool machine practitioners.
If you fail to enter the code on the keypad in time, it alerts the police or an offsite security professional to a potential intruder.False alarms are obviously uncomfortable but insufficient, and they can lead to a lack of response when a real security problem occurs.Thanks to AI programming, this has begun to change.With the security system of the future, every aspect of your home care system will be connected through IoT.Once the system has a profile of regular functionality, it can use what it has learned to manage the home security process more effectively.You start to see things like locks, which come together with cameras to detect different visitors, not just knowing the normal times that different people come and go.It allows information program access decisions to be made.The system may also notice that you have forgotten to lock your doors when you leave home and send a reminder to your smartphone.This helps the system when it needs to decide whether to trigger an alarm.
It helps to develop computer programs and to help computers to learn without human intervention.The future of machine learning is very bright.Machine Learning Applications:     Machine Learning in EducationMachine learning is used in the education sector.You can look forward to more new and interesting features in the upcoming years.are gearing up their focus on healthcare services.Computer vision which uses deep learning is the most significant contributor in machine learning.Advantages of Machine learning Easily identifies trends and patterns: Machine learning helps to do things what human can’t do.
It will be an essential part of human life in the future.Artificial Intelligence has grown 270% in four years an industry remains competitive.Mechanized thinking use AI to copy human bits of knowledge.PCs need to realize how to respond to explicit exercises.Profound learning utilizes to "educate" PCs on how to comprehend client inquiries, text, pictures, and discourse designs.It's more likely to react to purchaser requests and make predictable.
The market for technologies like artificial intelligence (AI) is flourishing.Many internet giants and startups are racing to acquire AI; there is a significant increase in investment and adoption by organizations.As enterprises view business intelligence, AI is transforming their way.Currently, artificial Intelligence includes a variety of technologies and tools, some time-tested, and the rest are relatively new.Here are technologies that define Artificial Intelligence.Read more: click here
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It’s boggling that the bulk of the world’s wealth is stored in databases, and transactions are simply the exchanges of information over networks.As impressive — or scary — as that might sound, artificial intelligence technologies aim to further revolutionize the way banking is done and the relationships between banks and their customers’ experience.Always-on chatbot sidesteps banking hoursThere’s a reason why people deride banking hours.Our money doesn’t sleep, so why should the banks?Fortunately, AI in banking is one of the most impactful applications of artificial intelligence through the use of conversational assistants, or chatbots, to engage customers 24/7.Customers are increasingly comfortable with chatbots handling many things, even private conversations regarding bank transactions, bank services, and other tasks that don’t necessarily require human intervention.For example, Bank of America introduced Erica as a virtual assistant to help with customer transactions, and that has shown significant positive ROI.Many banks have quickly followed suit, although some with mixed results.In addition to fielding customer service inquiries and conversations about individual transactions, banks have been finding good results in using chatbots to make their customers aware of additional services and offerings.For example, business customers might not be aware of merchant services and loan offerings that can help resolve payment or credit issues.
The conversation is one of the main factors which is responsible for driving sales in the modern business world.For your customers, it is imperative to ask some questions that they have about products and services.So, there should be someone who will be able to answer these questions, make recommendations, and provide a personalized experience to your customers.So, in this world where conversations are the driving factor for commerce, chatbots are increasingly gaining popularity due to the important benefits that it provides.About 71% of the people are currently using chatbots to solve their problems when it comes to online purchases.Here, let's discuss about chatbots and understand why investing in chatbot development services would be great for the business of yours.The text above is a summary, you can read the full article here.
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If you fail to enter the code on the keypad in time, it alerts the police or an offsite security professional to a potential intruder.False alarms are obviously uncomfortable but insufficient, and they can lead to a lack of response when a real security problem occurs.Thanks to AI programming, this has begun to change.With the security system of the future, every aspect of your home care system will be connected through IoT.Once the system has a profile of regular functionality, it can use what it has learned to manage the home security process more effectively.You start to see things like locks, which come together with cameras to detect different visitors, not just knowing the normal times that different people come and go.It allows information program access decisions to be made.The system may also notice that you have forgotten to lock your doors when you leave home and send a reminder to your smartphone.This helps the system when it needs to decide whether to trigger an alarm.
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The process of data annotation, for speech, image, video, audio, or text, is a highly specialized task that needs expertise.When we talk about high quality data, it means that the labels are both accurate and consistent.By outsourcing data annotation services, companies save time and effort freeing up their in-house data scientists to focus on areas they are experienced in instead of annotating data.Here are a few of data annotation techniques used for specific AI initiatives.Audio Transcription for Speech Recognition Audio transcription is used to train speech recognition models.High-accuracy audio transcription enables interactions to accurately happen between human speech and AI-based models like smart TVs, phones, virtual assistants, watches, computers, or other in-home or on-the-road technology.Human speech must be accurately recognized to understand not only what words are being spoken but also what they mean.Text annotation detects and labels words depending on a predetermined set of categories required by an AI company.Text annotation for Natural Language Processing or NLP helps machines predict and understand the human language more easily.
Mankind is literally at the cusp of rapid technological advancement.Machine Learning — A Trending Tech Skill in 2020Around 73% of all cubicle-related jobs will be automated by 2030 which is equivalent to over 20 million jobs.(infographic)On the brighter side, this is also true that only humans hold the power of thinking and creating.Keeping pace with the technological advancements and getting versed in trending technology shall unequivocally let the working class stay valuable in the global technology job market.And when we talk about tech skills, mentioning Machine learning certainly becomes imperative.What is Machine Learning?Google the meaning of Machine Learning, and you will end up finding countless definitions in technical language.Thus to address the fast-evolving demands of the customers, the logistic industry is putting ample effort into adapting to technologies like ML and AI for enriching real-time decision making on issues like inventories, carriers, availability and costs , to keeping track of warehouse locations.Forbes insights research found out 65% of senior transportation-focused executives agree that the evolution of AI, ML and, related technologies can bring significant revolution in the logistics, supply chain and transportation processes.Machine Learning in Retail IndustryRetail businesses across the world are leveraging the potentialities of Machine Learning and it is poised to disrupt the retail industry in many ways.One primary zone where ML is contributing is in the materializing theory of smart automation in inventory management and supply chain.For instance, a retail king like Amazon has access to a myriad of customer data and applying ML to that data, they can predict demand for particular products, provide customized recommendations and so on.
Improved operations, efficient cost management vs. focus on profitability:Banks essentially have to make a profit to survive, and today, banks face significant pressure on their margins.Automation of about 80% of repetitive work processes helps officers dedicate their time to value-added operations that require a high level of human intervention like product marketing.What we need now is not just empowering of banks by automation, but making the entire system intelligent enough to beat the newly emerging FinTech players.It can help in costly and error-prone banking services like claims management by drastically reducing the time spent in reading or recording client information.For instance, JPMorgan Chase’s COiN reviews documents and extracts data from 12,000 documents (which, without automation, would require more than 360,000 hours of work) in just seconds.Lending:A minuscule percentage of the Indian population has an idea of credit.It is also an annoying task for banks to analyze an individual’s creditworthiness due to the lack of credit history.The use of Big Data and Machine Learning to analyze spending patterns and behavioral data of a customer over 10,000+ data points can help banks have an insight into the customer’s creditworthiness.In the case of SME and corporate loans, AI simplifies the complex and critical borrowing process, identify the potential risks in giving the loan by analyzing market trends, prospect’s behavior, and identifies even the slightest probability of fraud.Risk Management And Fraud DetectionThe Punjab National Bank scam exposed the banking sector to an enormous amount of risk and shook the regulators, financial and stock markets, and the banking industry.AI solutions can also be a game-changer by detecting insider trading that leads to market abuse.Insurance underwriting and claims:In this era of bancassurance, customers are more likely to come to banks rather than visit insurance agencies.
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Artificial Intelligence is also known as machine intelligence, where machines mimic the intelligence of the human mind and actions.The primary purpose of artificial intelligence is to make rational decisions according to the circumstances.The correct implementation of AI can carry more complex tasks with less time.Artificial intelligence has received an upward trend of growth, leading to the development of human society and multiple industries, and artificial intelligence will continue to grow in promising ways for consumers and various businesses.As 2020 has not been a great year so far because of COVID-19, artificial intelligence has played a massive role in combating the pandemic induced situations.Every ounce of technological advances and innovations helped us to fight the pandemic.Every organization is finding new ways to operate effectively and to serve their customers as social distancing measures remain in place.These chatbots have provided the answer to almost every possible question, from evaluation to exercise without putting the human resource at risk.Machine learning and artificial intelligence have helped researchers to analyze a large volume of data to take preventive measures.
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