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Cell Biology Cloud Computing Market Development, Market Trends, Key Driven Factors, Segmentation And Forecast To 2020-2026| Accenture, Amazon Web…

May 7, 2020 | Author: admin

Latest Report On Cell Biology Cloud Computing Market including Market Landscape, and Market size, Revenues by players, Revenues by regions, Average prices, Competitive landscape, market Dynamics and industry trends and developments during the forecast period.

The global Cell Biology Cloud Computing market is broadly analyzed in this report that sheds light on critical aspects such as the vendor landscape, competitive strategies, market dynamics, and regional analysis. The report helps readers to clearly understand the current and future status of the global Cell Biology Cloud Computing market. The research study comes out as a compilation of useful guidelines for players to secure a position of strength in the global market. The authors of the report profile leading companies of the global Cell Biology Cloud Computing market, Also the details about important activities of leading players in the competitive landscape.

Key companies operating in the global Cell Biology Cloud Computing market include: , Accenture, Amazon Web Services, Benchling, Cisco Systems, Dell Emc, IBM, DXC Technology, Oracle, ScaleMatrix, IPERION, NovelBio

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The report predicts the size of the global Cell Biology Cloud Computing market in terms of value and volume for the forecast period 2020-2026. As per the analysis provided in the report, the global Cell Biology Cloud Computing market is expected to rise at a CAGR of xx % between 2020 and 2026 to reach a valuation of US$ xx million/billion by the end of 2026. In 2020, the global Cell Biology Cloud Computing market attained a valuation of US$ XX million/billion. The market researchers deeply analyze the global Cell Biology Cloud Computing industry landscape and the future prospects it is anticipated to create

Segmental Analysis

The report has classified the global Cell Biology Cloud Computing industry into segments including product type and application. Every segment is evaluated based on growth rate and share. Besides, the analysts have studied the potential regions that may prove rewarding for the Cell Biology Cloud Computing manufcaturers in the coming years. The regional analysis includes reliable predictions on value and volume, thereby helping market players to gain deep insights into the overall Cell Biology Cloud Computing industry.

Global Cell Biology Cloud Computing Market Segment By Type:

, Public Cloud Computing, Private Cloud Computing, Hybrid Cloud Computing

Global Cell Biology Cloud Computing Market Segment By Application:

,Genomics, Diagnostics, Clinical Trials, Pharma Manufacturing, Others

Competitive Landscape

It is important for every market participant to be familiar with the competitive scenario in the global Cell Biology Cloud Computing industry. In order to fulfil the requirements, the industry analysts have evaluated the strategic activities of the competitors to help the key players strengthen their foothold in the market and increase their competitiveness.

Key companies operating in the global Cell Biology Cloud Computing market include: , Accenture, Amazon Web Services, Benchling, Cisco Systems, Dell Emc, IBM, DXC Technology, Oracle, ScaleMatrix, IPERION, NovelBio

Key questions answered in the report:

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TOC

1 Market Overview of Cell Biology Cloud Computing1.1 Cell Biology Cloud Computing Market Overview1.1.1 Cell Biology Cloud Computing Product Scope1.1.2 Market Status and Outlook1.2 Global Cell Biology Cloud Computing Market Size Overview by Region 2015 VS 2020 VS 20261.3 Global Cell Biology Cloud Computing Market Size by Region (2015-2026)1.4 Global Cell Biology Cloud Computing Historic Market Size by Region (2015-2020)1.5 Global Cell Biology Cloud Computing Market Size Forecast by Region (2021-2026)1.6 Key Regions Cell Biology Cloud Computing Market Size YoY Growth (2015-2026)1.6.1 North America Cell Biology Cloud Computing Market Size YoY Growth (2015-2026)1.6.2 Europe Cell Biology Cloud Computing Market Size YoY Growth (2015-2026)1.6.3 China Cell Biology Cloud Computing Market Size YoY Growth (2015-2026)1.6.4 Rest of Asia Pacific Cell Biology Cloud Computing Market Size YoY Growth (2015-2026)1.6.5 Latin America Cell Biology Cloud Computing Market Size YoY Growth (2015-2026)1.6.6 Middle East & Africa Cell Biology Cloud Computing Market Size YoY Growth (2015-2026)1.7 Coronavirus Disease 2019 (Covid-19): Cell Biology Cloud Computing Industry Impact1.7.1 How the Covid-19 is Affecting the Cell Biology Cloud Computing Industry

1.7.1.1 Cell Biology Cloud Computing Business Impact Assessment Covid-19

1.7.1.2 Supply Chain Challenges

1.7.1.3 COVID-19s Impact On Crude Oil and Refined Products1.7.2 Market Trends and Cell Biology Cloud Computing Potential Opportunities in the COVID-19 Landscape1.7.3 Measures / Proposal against Covid-19

1.7.3.1 Government Measures to Combat Covid-19 Impact

1.7.3.2 Proposal for Cell Biology Cloud Computing Players to Combat Covid-19 Impact 2 Cell Biology Cloud Computing Market Overview by Type2.1 Global Cell Biology Cloud Computing Market Size by Type: 2015 VS 2020 VS 20262.2 Global Cell Biology Cloud Computing Historic Market Size by Type (2015-2020)2.3 Global Cell Biology Cloud Computing Forecasted Market Size by Type (2021-2026)2.4 Public Cloud Computing2.5 Private Cloud Computing2.6 Hybrid Cloud Computing 3 Cell Biology Cloud Computing Market Overview by Type3.1 Global Cell Biology Cloud Computing Market Size by Application: 2015 VS 2020 VS 20263.2 Global Cell Biology Cloud Computing Historic Market Size by Application (2015-2020)3.3 Global Cell Biology Cloud Computing Forecasted Market Size by Application (2021-2026)3.4 Genomics3.5 Diagnostics3.6 Clinical Trials3.7 Pharma Manufacturing3.8 Others 4 Global Cell Biology Cloud Computing Competition Analysis by Players4.1 Global Cell Biology Cloud Computing Market Size (Million US$) by Players (2015-2020)4.2 Global Top Manufacturers by Company Type (Tier 1, Tier 2 and Tier 3) (based on the Revenue in Cell Biology Cloud Computing as of 2019)4.3 Date of Key Manufacturers Enter into Cell Biology Cloud Computing Market4.4 Global Top Players Cell Biology Cloud Computing Headquarters and Area Served4.5 Key Players Cell Biology Cloud Computing Product Solution and Service4.6 Competitive Status4.6.1 Cell Biology Cloud Computing Market Concentration Rate4.6.2 Mergers & Acquisitions, Expansion Plans 5 Company (Top Players) Profiles and Key Data5.1 Accenture5.1.1 Accenture Profile5.1.2 Accenture Main Business and Companys Total Revenue5.1.3 Accenture Products, Services and Solutions5.1.4 Accenture Revenue (US$ Million) (2015-2020)5.1.5 Accenture Recent Developments5.2 Amazon Web Services5.2.1 Amazon Web Services Profile5.2.2 Amazon Web Services Main Business and Companys Total Revenue5.2.3 Amazon Web Services Products, Services and Solutions5.2.4 Amazon Web Services Revenue (US$ Million) (2015-2020)5.2.5 Amazon Web Services Recent Developments5.3 Benchling5.5.1 Benchling Profile5.3.2 Benchling Main Business and Companys Total Revenue5.3.3 Benchling Products, Services and Solutions5.3.4 Benchling Revenue (US$ Million) (2015-2020)5.3.5 Cisco Systems Recent Developments5.4 Cisco Systems5.4.1 Cisco Systems Profile5.4.2 Cisco Systems Main Business and Companys Total Revenue5.4.3 Cisco Systems Products, Services and Solutions5.4.4 Cisco Systems Revenue (US$ Million) (2015-2020)5.4.5 Cisco Systems Recent Developments5.5 Dell Emc5.5.1 Dell Emc Profile5.5.2 Dell Emc Main Business and Companys Total Revenue5.5.3 Dell Emc Products, Services and Solutions5.5.4 Dell Emc Revenue (US$ Million) (2015-2020)5.5.5 Dell Emc Recent Developments5.6 IBM5.6.1 IBM Profile5.6.2 IBM Main Business and Companys Total Revenue5.6.3 IBM Products, Services and Solutions5.6.4 IBM Revenue (US$ Million) (2015-2020)5.6.5 IBM Recent Developments5.7 DXC Technology5.7.1 DXC Technology Profile5.7.2 DXC Technology Main Business and Companys Total Revenue5.7.3 DXC Technology Products, Services and Solutions5.7.4 DXC Technology Revenue (US$ Million) (2015-2020)5.7.5 DXC Technology Recent Developments5.8 Oracle5.8.1 Oracle Profile5.8.2 Oracle Main Business and Companys Total Revenue5.8.3 Oracle Products, Services and Solutions5.8.4 Oracle Revenue (US$ Million) (2015-2020)5.8.5 Oracle Recent Developments5.9 ScaleMatrix5.9.1 ScaleMatrix Profile5.9.2 ScaleMatrix Main Business and Companys Total Revenue5.9.3 ScaleMatrix Products, Services and Solutions5.9.4 ScaleMatrix Revenue (US$ Million) (2015-2020)5.9.5 ScaleMatrix Recent Developments5.10 IPERION5.10.1 IPERION Profile5.10.2 IPERION Main Business and Companys Total Revenue5.10.3 IPERION Products, Services and Solutions5.10.4 IPERION Revenue (US$ Million) (2015-2020)5.10.5 IPERION Recent Developments5.11 NovelBio5.11.1 NovelBio Profile5.11.2 NovelBio Main Business and Companys Total Revenue5.11.3 NovelBio Products, Services and Solutions5.11.4 NovelBio Revenue (US$ Million) (2015-2020)5.11.5 NovelBio Recent Developments 6 North America Cell Biology Cloud Computing by Players and by Application6.1 North America Cell Biology Cloud Computing Market Size and Market Share by Players (2015-2020)6.2 North America Cell Biology Cloud Computing Market Size by Application (2015-2020) 7 Europe Cell Biology Cloud Computing by Players and by Application7.1 Europe Cell Biology Cloud Computing Market Size and Market Share by Players (2015-2020)7.2 Europe Cell Biology Cloud Computing Market Size by Application (2015-2020) 8 China Cell Biology Cloud Computing by Players and by Application8.1 China Cell Biology Cloud Computing Market Size and Market Share by Players (2015-2020)8.2 China Cell Biology Cloud Computing Market Size by Application (2015-2020) 9 Rest of Asia Pacific Cell Biology Cloud Computing by Players and by Application9.1 Rest of Asia Pacific Cell Biology Cloud Computing Market Size and Market Share by Players (2015-2020)9.2 Rest of Asia Pacific Cell Biology Cloud Computing Market Size by Application (2015-2020) 10 Latin America Cell Biology Cloud Computing by Players and by Application10.1 Latin America Cell Biology Cloud Computing Market Size and Market Share by Players (2015-2020)10.2 Latin America Cell Biology Cloud Computing Market Size by Application (2015-2020) 11 Middle East & Africa Cell Biology Cloud Computing by Players and by Application11.1 Middle East & Africa Cell Biology Cloud Computing Market Size and Market Share by Players (2015-2020)11.2 Middle East & Africa Cell Biology Cloud Computing Market Size by Application (2015-2020) 12 Cell Biology Cloud Computing Market Dynamics12.1 Industry Trends12.2 Market Drivers12.3 Market Challenges12.4 Porters Five Forces Analysis 13 Research Finding /Conclusion 14 Methodology and Data Source 14.1 Methodology/Research Approach14.1.1 Research Programs/Design14.1.2 Market Size Estimation14.1.3 Market Breakdown and Data Triangulation14.2 Data Source14.2.1 Secondary Sources14.2.2 Primary Sources14.3 Disclaimer14.4 Author List

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Cell Biology Cloud Computing Market Development, Market Trends, Key Driven Factors, Segmentation And Forecast To 2020-2026| Accenture, Amazon Web...

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Robots and Artificial Intelligence | HowStuffWorks

May 6, 2020 | Author: admin

Artificial intelligence (AI) is arguably the most exciting field in robotics. It's certainly the most controversial: Everybody agrees that a robot can work in an assembly line, but there's no consensus on whether a robot can ever be intelligent.

Like the term "robot" itself, artificial intelligence is hard to define. Ultimate AI would be a recreation of the human thought process -- a man-made machine with our intellectual abilities. This would include the ability to learn just about anything, the ability to reason, the ability to use language and the ability to formulate original ideas. Roboticists are nowhere near achieving this level of artificial intelligence, but they have made a lot of progress with more limited AI. Today's AI machines can replicate some specific elements of intellectual ability.

Computers can already solve problems in limited realms. The basic idea of AI problem-solving is very simple, though its execution is complicated. First, the AI robot or computer gathers facts about a situation through sensors or human input. The computer compares this information to stored data and decides what the information signifies. The computer runs through various possible actions and predicts which action will be most successful based on the collected information. Of course, the computer can only solve problems it's programmed to solve -- it doesn't have any generalized analytical ability. Chess computers are one example of this sort of machine.

Some modern robots also have the ability to learn in a limited capacity. Learning robots recognize if a certain action (moving its legs in a certain way, for instance) achieved a desired result (navigating an obstacle). The robot stores this information and attempts the successful action the next time it encounters the same situation. Again, modern computers can only do this in very limited situations. They can't absorb any sort of information like a human can. Some robots can learn by mimicking human actions. In Japan, roboticists have taught a robot to dance by demonstrating the moves themselves.

Some robots can interact socially. Kismet, a robot at M.I.T's Artificial Intelligence Lab, recognizes human body language and voice inflection and responds appropriately. Kismet's creators are interested in how humans and babies interact, based only on tone of speech and visual cue. This low-level interaction could be the foundation of a human-like learning system.

Kismet and other humanoid robots at the M.I.T. AI Lab operate using an unconventional control structure. Instead of directing every action using a central computer, the robots control lower-level actions with lower-level computers. The program's director, Rodney Brooks, believes this is a more accurate model of human intelligence. We do most things automatically; we don't decide to do them at the highest level of consciousness.

The real challenge of AI is to understand how natural intelligence works. Developing AI isn't like building an artificial heart -- scientists don't have a simple, concrete model to work from. We do know that the brain contains billions and billions of neurons, and that we think and learn by establishing electrical connections between different neurons. But we don't know exactly how all of these connections add up to higher reasoning, or even low-level operations. The complex circuitry seems incomprehensible.

Because of this, AI research is largely theoretical. Scientists hypothesize on how and why we learn and think, and they experiment with their ideas using robots. Brooks and his team focus on humanoid robots because they feel that being able to experience the world like a human is essential to developing human-like intelligence. It also makes it easier for people to interact with the robots, which potentially makes it easier for the robot to learn.

Just as physical robotic design is a handy tool for understanding animal and human anatomy, AI research is useful for understanding how natural intelligence works. For some roboticists, this insight is the ultimate goal of designing robots. Others envision a world where we live side by side with intelligent machines and use a variety of lesser robots for manual labor, health care and communication. A number of robotics experts predict that robotic evolution will ultimately turn us into cyborgs -- humans integrated with machines. Conceivably, people in the future could load their minds into a sturdy robot and live for thousands of years!

In any case, robots will certainly play a larger role in our daily lives in the future. In the coming decades, robots will gradually move out of the industrial and scientific worlds and into daily life, in the same way that computers spread to the home in the 1980s.

The best way to understand robots is to look at specific designs. The links below will show you a variety of robot projects around the world.

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Robots and Artificial Intelligence | HowStuffWorks

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A Brief History of Artificial Intelligence | Live Science

May 6, 2020 | Author: admin

The idea of inanimate objects coming to life as intelligent beings has been around for a long time. The ancient Greeks had myths about robots, and Chinese and Egyptian engineers built automatons.

The beginnings of modern AI can be traced to classical philosophers' attempts to describe human thinking as a symbolic system. But the field of AI wasn't formally founded until 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, where the term "artificial intelligence" was coined.

MIT cognitive scientist Marvin Minsky and others who attended the conference were extremely optimistic about AI's future. "Within a generation[...] the problem of creating 'artificial intelligence' will substantially be solved," Minsky is quoted as saying in the book "AI: The Tumultuous Search for Artificial Intelligence" (Basic Books, 1994). [Super-Intelligent Machines: 7 Robotic Futures]

But achieving an artificially intelligent being wasn't so simple. After several reports criticizing progress in AI, government funding and interest in the field dropped off a period from 197480 that became known as the "AI winter." The field later revived in the 1980s when the British government started funding it again in part to compete with efforts by the Japanese.

The field experienced another major winter from 1987 to 1993, coinciding with the collapse of the market for some of the early general-purpose computers, and reduced government funding.

But research began to pick up again after that, and in 1997, IBM's Deep Blue became the first computer to beat a chess champion when it defeated Russian grandmaster Garry Kasparov. And in 2011, the computer giant's question-answering system Watson won the quiz show "Jeopardy!" by beating reigning champions Brad Rutter and Ken Jennings.

This year, the talking computer "chatbot" Eugene Goostman captured headlines for tricking judges into thinking he was real skin-and-blood human during a Turing test, a competition developed by British mathematician and computer scientist Alan Turing in 1950 as a way to assess whether a machine is intelligent.

But the accomplishment has been controversial, with artificial intelligence experts saying that only a third of the judges were fooled, and pointing out that the bot was able to dodge some questions by claiming it was an adolescent who spoke English as a second language.

Manyexperts now believe the Turing test isn't a good measure of artificial intelligence.

"The vast majority of people in AI who've thought about the matter, for the most part, think its a very poor test, because it only looks at external behavior," Perlis told Live Science.

In fact, some scientists now plan to develop an updated version of the test. But the field of AI has become much broader than just the pursuit of true, humanlike intelligence.

Follow Tanya Lewis on Twitterand Google+. Follow us @livescience, Facebook& Google+. Original article onLive Science.

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A Brief History of Artificial Intelligence | Live Science

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Top 18 Artificial Intelligence Platforms in 2020 – Reviews …

May 6, 2020 | Author: admin

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Artificial Intelligence Algorithms For Beginners | Edureka

May 6, 2020 | Author: admin

We can all agree that Artificial Intelligence has created a huge impact on the worlds economy and will continue to do so since were aiding its growth by producing an immeasurable amount of data. Thanks to the advancement in Artificial Intelligence Algorithms we can deal with such humungous data. In this blog post, you will understand the different Artificial Intelligence Algorithms and how they can be used to solve real-world problems.

To get in-depth knowledge of Artificial Intelligence and Machine Learning, you can enroll for liveMachine Learning Engineer Master Programby Edureka with 24/7 support and lifetime access.

Heres a list of topics that will be covered in this post:

To simply put it, Artificial Intelligence is the science of getting machines to think and make decisions like human beings do.

Since the development of complex Artificial Intelligence Algorithms, it has been able to accomplish this by creating machines and robots that are applied in a wide range of fields including agriculture, healthcare, robotics, marketing, business analytics and many more.

Before we move any further lets try to understand what Machine Learning is and how does it is related to AI.

Generally, an algorithm takes some input and uses mathematics and logic to produce the output. In stark contrast, an Artificial Intelligence Algorithm takes a combination of both inputs and outputs simultaneously in order to learn the data and produce outputs when given new inputs.

This process of making machines learn from data is what we call MachineLearning.

Artificial Intelligence Algorithm Artificial Intelligence Algorithms Edureka

Machine Learning is a sub-field of Artificial Intelligence, where we try to bring AI into the equation by learning the input data.

If youre curious to learn more about Machine Learning, give the following blogs a read:

Machines can follow different approaches to learn depending on the data set and the problem that is being solved. In the below section well understand the different ways in which machines can learn.

Machine Learning can be done in the following ways:

Lets briefly understand the idea behind each type of Machine Learning.

In Supervised Learning, as the name rightly suggests, it involves making the algorithm learn the data while providing the correct answers or the labels to the data. This essentially means that the classes or the values to be predicted are known and well defined for the algorithm from the very beginning.

The other class falls under Unsupervised Learning, where, unlike supervised methods the algorithm doesnt have correct answers or any answers at all, it is up to the algorithms discretion to bring together similar data and understand it.

Along with these two prominent classes, we also have a third class, called Reinforcement Learning. Just as children are generally reinforced certain ideas, principles by either rewarding them when doing the right thing or punishing upon doing something wrong, in Reinforcement Learning, there are rewards given to the algorithm upon every correct prediction thus driving the accuracy higher up.

Heres a short video recorded by our Machine Learning experts. This will help you understand the difference between Supervised, Unsupervised and Reinforcement learning.

While the above three classes cover most fields comprehensively, we sometimes still land into the issue of having to bump up the performance of our model. In such cases it might make sense, to use ensemble methods (explained later) to get the accuracy higher up.

Now lets understand how Artificial Intelligence algorithms can be used to solve different types of problems.

Algorithms in each category, in essence, perform the same task of predicting outputs given unknown inputs, however, here data is the key driver when it comes to picking the right algorithm.

What follows is an outline of categories of Machine Learning problems with a brief overview of the same:

Heres a table that effectively differentiates each of these categories of problems.

Type Of Problems Solved Using AI Artificial Intelligence Algorithms Edureka

For each category of tasks, we can use specific algorithms. In the below section youll understand how a category of algorithms can be used as a solution to complex problems.

As mentioned above, different Artificial Intelligence algorithms can be used to solve a category of problems. In the below section well see the different types of algorithms that fall under Classification, Regression and Clustering problems.

Classification, as the name suggests is the act of dividing the dependent variable (the one we try to predict) into classes and then predict a class for a given input. It falls into the category of Supervised Machine Learning, where the data set needs to have the classes, to begin with.Thus, classification comes into play at any place where we need to predict an outcome, from a set number of fixed, predefined outcomes.

Classification uses an array of algorithms, a few of them listed below

Let us break them down and see where they fit in when it comes to application.

Naive Bayes algorithm follows the Bayes theorem, which unlike all the other algorithms in this list, follows a probabilistic approach. This essentially means, that instead of jumping straight into the data, the algorithm has a set of prior probabilities set for each of the classes for your target.

Once you feed in the data, the algorithm updates these prior probabilities to form something known as the posterior probability.Hence this can be extremely useful in cases where you need to predict whether your input belongs to either a given list of n classes or does it not belong to any of them. This can be possible using a probabilistic approach mainly because the probabilities thrown for all the n classes will be quite low.

Let us try to understand this with an example, of a person playing golf, depending on factors like the weather outside.

We first try to generate the frequencies with which certain events occur, in this case, we try to find frequencies of the person playing golf if its sunny, rainy, etc outside.

Naive Bayes Artificial Intelligence Algorithms Edureka

Using these frequencies we generate our apriori or initial probabilities (eg, the probability of overcast is 0.29 while the general probability of playing is 0.64)

Next up, we generate the posterior probabilities, where we try to answer questions like what would be the probability of it being sunny outside and the person would play golf?

We use the Bayesian formula here,

P(Yes | Sunny) = P( Sunny | Yes) * P(Yes) / P (Sunny)Here we have P (Sunny |Yes) = 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P( Yes)= 9/14 = 0.64

You can go through this A Comprehensive Guide To Naive Bayes blog to help you understand the math behind Naive Bayes.

The Decision Tree can essentially be summarized as a flowchart-like tree structure where each external node denotes a test on an attribute and each branch represents the outcome of that test. The leaf nodes contain the actual predicted labels. We start from the root of the tree and keep comparing attribute values until we reach a leaf node.

Decision Trees Artificial Intelligence Algorithms Edureka

We use this classifier when handling high dimensional data and when little time has been spent behind data preparation. However, a word of caution they tend to overfit and are prone to change drastically even with slight nuances in the training data.

You can through these blogs to learn more about Decision Trees:

Think of this as a committee of Decision Trees, where each decision tree has been fed a subset of the attributes of data and predicts on the basis of that subset. The average of the votes of all decision trees are taken into account and the answer is given.

An advantage of using Random Forest is that it alleviates the problem of overfitting which was present in a standalone decision tree, leading to a much more robust and accurate classifier.

Random Forest Artificial Intelligence Algorithms Edureka

As we can see in the above image, we have 5 decision trees trying to classify a color. Here 3 of these 5 decision trees predict blue and two have different outputs, namely green and red. In this case, we take the average of all the outputs, which gives blue as the highest weightage.

Heres a blog on Random Forest Classifier that will help you understand the working of Random forest algorithm and how it can be used to solve real-world problems.

Its a go-to method mainly for binary classification tasks. The term logistic comes from the logit function that is used in this method of classification. The logistic function, also called as the sigmoid function is an S-shaped curve that can take any real-valued number and map it between 0 and 1 but never exactly at those limits.

Logistic Regression Artificial Intelligence Algorithms Edureka

Lets assume that your little brother is trying to get into grad school, and you want to predict whether hell get admitted in his dream establishment. So, based on his CGPA and the past data, you can use Logistic Regression to foresee the outcome.

Logistic Regression allows you to analyze a set of variables and predict a categorical outcome. Since here we need to predict whether he will get into the school or not, which is a classification problem, logistic regression would be ideal.

Logistic Regression is used to predict house values, customer lifetime value in the insurance sector, etc.

An SVM is unique, in the sense that it tries to sort the data with the margins between two classes as far apart as possible. This is called maximum margin separation.

Another thing to take note of here is the fact that SVMs take into account only the support vectors while plotting the hyperplane, unlike linear regression which uses the entire dataset for that purpose. This makes SVMs quite useful in situations when data is in high dimensions.

Lets try to understand this with an example. In the below figure we have to classify data points into two different classes (squares and triangles).

Support Vector Machine Artificial Intelligence Algorithms Edureka

So, you start off by drawing a random hyperplane and then you check the distance between the hyperplane and the closest data points from each class. These closest data points to the hyperplane are known as Support vectors. And thats where the name comes from, Support Vector Machine.

The hyperplane is drawn based on these support vectors and an optimum hyperplane will have a maximum distance from each of the support vectors. And this distance between the hyperplane and the support vectors is known as the margin.

To sum it up, SVM is used to classify data by using a hyperplane, such that the distance between the hyperplane and the support vectors is maximum.

To learn more about SVM, you can go through this, Using SVM To Predict Heart Diseases blog.

KNN is a non-parametric (here non-parametric is just a fancy term which essentially means that KNN does not make any assumptions on the underlying data distribution), lazy learning algorithm (here lazy means that the training phase is fairly short).

Its purpose is to use a whole bunch of data points separated into several classes to predict the classification of a new sample point.

The following points serve as an overview of the general working of the algorithm:

However, there are some downsides to using KNN. These downsides mainly revolve around the fact that KNN works on storing the entire dataset and comparing new points to existing ones. This means that the storage space increases as our training set increases. This also means that the estimation time increases in proportion to the number of training points.

The following blogs will help you understand how the KNN algorithm works in depth:

Now lets understand how regression problems can be solved by using regression algorithms.

In the case of regression problems, the output is a continuous quantity. Meaning that we can use regression algorithms in cases where the target variable is a continuous variable. It falls into the category of Supervised Machine Learning, where the data set needs to have the labels, to begin with.

Linear Regression is the most simple and effective regression algorithm. It is utilized to gauge genuine qualities (cost of houses, number of calls, all out deals and so forth.) in view of the consistent variable(s). Here, we build up a connection between free and ward factors by fitting the best line. This best fit line is known as regression line and spoken to by a direct condition Y= a *X + b.

Linear Regression Artificial Intelligence Algorithms Edureka

Let us take a simple example here to understand linear regression.

Consider that you are given the challenge to estimate an unknown persons weight by just looking at them. With no other values in hand, this might look like a fairly difficult task, however using your past experience you know that generally speaking the taller someone is, the heavier they are compared to a shorter person of the same build. This is linear regression, in actuality!

However, linear regression is best used in approaches involving a low number of dimensions. Also, not every problem is linearly separable.

Some of the most popular applications of Linear regression are in financial portfolio prediction, salary forecasting, real estate predictions and in traffic in arriving at ETAs

Now lets discuss how clustering problems can be solved by using the K-means algorithm. Before that, lets understand what clustering is.

The basic idea behind clustering is to assign the input into two or more clusters based on feature similarity. It falls into the category of Unsupervised Machine Learning, where the algorithm learns the patterns and useful insights from data without any guidance (labeled data set).

For example, clustering viewers into similar groups based on their interests, age, geography, etc can be done by using Unsupervised Learning algorithms like K-Means Clustering.

K-means is probably the simplest unsupervised learning approach. The idea here is to gather similar data points together and bind them together in the form of a cluster. It does this by calculating the centroid of the group of data points.

To carry out effective clustering, k-means evaluates the distance between each point from the centroid of the cluster. Depending on the distance between the data point and the centroid, the data is assigned to the closest cluster. The goal of clustering is to determine the intrinsic grouping in a set of unlabelled data.

K-means Artificial Intelligence Algorithms Edureka

The K in K-means stands for the number of clusters formed. The number of clusters (basically the number of classes in which your new instances of data can fall into) is determined by the user.

K-means is used majorly in cases where the data set has points which are distinct and well separated from each other, otherwise, the clusters wont be far apart, rendering them inaccurate. Also, K-means should be avoided in cases where the data set contains a high amount of outliers or the data set is non-linear.

So that was a brief about K-means algorithm, to learn more you can go through this content recorded by our Machine Learning experts.

In cases where data is of abundance and prediction precision is of high value, boosting algorithms come into the picture.

Consider the scenario, you have a decision tree trained on a data set along with a whole bunch of hyperparameter tuning already performed, however, the final accuracy is still slightly off than youd like. In this case, while it might seem that you have run out of possible things to try, ensemble learning comes to the rescue.

Ensemble Learning Artificial Intelligence Algorithms Edureka

You have two different ways in which you can use ensemble learning, in this case, to bump up your accuracy. Let us say your decision tree was failing on a set of input test values, what you do now is, to train a new decision tree model and give a higher weighting to those input test values that your previous model struggled with. This is also called as Boosting, where our initial tree can be formally stated as a weak learner, and the mistakes caused by that model pave way for a better and stronger model.

Another way to approach this is by simply training a whole bunch of trees at once (this can be done fairly quickly and in a parallel fashion) and then taking outputs from each tree and averaging them out. So this way, if after training 10 trees, lets say 6 trees reply positive to input and 4 trees reply negative, the output you consider is positive. This is formally known as Bagging.

They are used to reduce the bias and variance in supervised learning techniques. There are a host of boosting algorithms available, a few of them discussed below:

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Artificial Intelligence Algorithms For Beginners | Edureka

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Women wanted: Why now could be a good time for women to pursue a career in AI – CNBC

May 6, 2020 | Author: admin

The coronavirus pandemic has upended countless jobs and even entire industries, leaving many wondering which will emerge out of the other side.

One industry likely to endure or even thrive under the virus, however, is artificial intelligence (AI), which could offer a glimpse into one of the rising careers of the future.

"This outbreak is creating overwhelming uncertainty and also greater demand for AI," IBM's vice president of data and AI, Ritika Gunnar told CNBC Make It.

Already, AI has been deployed sweepingly to help tackle the pandemic. Hospitals use the technology to diagnose patients; governments employ it in contact tracing apps and companies rely on it to support the biggest work from home experiment in history.

And that demand is only set to rise. Market research company International Data Corporation says it expects the number of AI jobs globally to grow 16% this year.

That could create new opportunities in an otherwise challenging jobs market. But the industry will need more women, in particular, if it is to overcome some of its historic bias challenges.

"In order to remove bias from AI, you need diverse perspectives among the people working on it. That means more women, and more diversity overall, in AI," said Gunnar.

The industry has been making progress lately. In a new report released Wednesday,IBMfound the majority (85%) of AI professionals think the industry has become more diverse over recent years, which has had a positive impact on the technology.

Of the more than 3,200 people surveyed acrossNorth America, Europe and India, 86% said they are now confident in AI systems' ability to make decisions without bias.

The AI opportunities from this crisis are numerous and the career opportunities are there.

Lisa Bouari

executive director, OutThought AI Assistants

However, Lisa Bouari, executive director at OutThought AI Assistants and a recipient of IBM's Women Leaders in AI awards, said more needs to be done to encourage women into the industry and keep them there.

"Attracting and retaining women are two halves of the same issue supporting a greater balance of women in AI," said Bouari. "The issues highlighted in the report around career progression, and hurdles, hold the keys to helping women stay in AI careers, and ultimately attracting more women as the status quo evolves."

For Gunnar, that means getting more women and girls excited about AI from a young age.

"We should expose girls to AI, math and science at a much earlier age so they have a support system in place," said Gunnar.

Indeed, IBM's report noted that although more women have been drawn to the industry over recent years, they did not consider AI a viable career path until later in life due to a lack of support during early education.

A plurality of men (46%) said they became interested in a tech career in high school or earlier, while a majority of women (53%) only considered it a possible path during their undergraduate degree or grad school.

But Bouari said she's hopeful that the surge in demand for AI currently can help drive the industry forward.

"The AI opportunities from this crisis are numerous and the career opportunities are there if we can successfully move hurdles and adopt it efficiently," she said.

Don't miss:Reaching gender equality at work means getting over this major hurdle first

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Global Artificial Intelligence in Agriculture Industry (2020 to 2026) – Developing Countries to Offer Significant Growth Opportunities – GlobeNewswire

May 6, 2020 | Author: admin

Dublin, May 06, 2020 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Agriculture Market by Technology (Machine Learning, Computer Vision, and Predictive Analytics), Offering (Software, Hardware, AI-as-a-Service, and Services), Application, and Geography - Global Forecast to 2026" report has been added to ResearchAndMarkets.com's offering.

The AI in the agriculture market is projected to grow at a CAGR of 25.5% from 2020 to 2026.

The AI in agriculture market growth is propelled by the increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep-learning technology, and government support for the adoption of modern agricultural techniques. However, the high cost of gathering precise field data restrains the market growth. Developing countries, such as China, Brazil, and India, are likely to provide an opportunity for the players in the AI in agriculture market due to the increasing use of unmanned aerial vehicles/drones by these countries in their agricultural farms.

By technology, the machine learning segment is estimated to account for the largest share of the AI in the agriculture market during the forecast period.

Machine learning-enabled solutions are being significantly adopted by agricultural organizations and farmers worldwide to enhance farm productivity and to gain a competitive edge in business operations. In the coming years, the application of machine learning in various agricultural practices is expected to rise exponentially.

By offering, the AI-as-a-Service segment is projected to register the highest CAGR from 2020 to 2026.

Increasing demand for machine learning tool kits and applications that are available in AI-based services, along with benefits, such as advanced infrastructure at minimal cost, transparency in business operations, and better scalability, is leading to the growth of the AI-as-a-Service segment.

By application, the precision farming segment held the largest market size in 2019.

Precision farming involves the usage of innovative artificial intelligence (AI) technologies, such as machine learning, computer vision, and predictive analytics tools, for increasing agriculture productivity. It comprises a technology-driven analysis of data acquired from the fields for increasing crop productivity. Precision farming helps in managing variations in the field accurately, thus enabling the growth of more crops using fewer resources and at reduced production costs. Precision devices integrated with AI technologies help in collecting farm-related data, thereby helping the farmers make better decisions and increase the productivity of their lands

Key Topics Covered:

1 Introduction

2 Research Methodology

3 Executive Summary

4 Premium Insights 4.1 Attractive Opportunities for the AI in the Agriculture Market4.2 AI in Agriculture Market, by Offering4.3 AI in Agriculture Market, by Technology4.4 AI in Agriculture Market for Apac, by Application & Country4.5 AI in Agriculture Market, by Geography

5 Market Overview 5.1 Introduction5.2 Market Dynamics5.2.1 Drivers5.2.1.1 Increasing Strain on Global Food Supply Owing to Rising Population5.2.1.2 Increasing Implementation of Data Generation Through Sensors and Aerial Images for Crops5.2.1.3 Increasing Crop Productivity Through Deep Learning Technology5.2.1.4 Government Support to Adopt Modern Agricultural Techniques5.2.2 Restraints5.2.2.1 High Cost of Gathering Precise Field Data5.2.3 Opportunities5.2.3.1 Developing Countries to Offer Significant Growth Opportunities5.2.3.2 Use of AI Solutions to Manage Small Farms (Less than 5 Hectares)5.2.4 Challenges5.2.4.1 Lack of Standardization5.2.4.2 Lack of Awareness About AI Among Farmers5.2.4.3 Limited Availability of Historical Data5.3 Value Chain Analysis5.4 Impact of Covid-19 on AI in Agriculture Market

6 Artificial Intelligence in Agriculture Market, by Technology 6.1 Introduction6.2 Machine Learning6.2.1 Machine Learning Technology to Hold the Largest Share of AI in Agriculture Market6.3 Computer Vision6.3.1 Computer Vision Technology is Expected to Grow at the Highest CAGR during the Forecast Period6.4 Predictive Analytics6.4.1 Increasing Predictive Analytics Applications is Expected to Drive the Growth of AI in Agriculture Market

7 Artificial Intelligence in Agriculture Market, by Offering 7.1 Introduction7.2 Hardware7.2.1 Technological Advancements in the Hardware Segment is Leading to the Widespread Adoption of AI in Agriculture7.2.2 Processor7.2.3 Storage Device7.2.4 Network7.3 Software7.3.1 AI in Agriculture Market for Software Segment is Projected to Hold the Largest Market Share during the Forecast Period7.3.2 AI Platform7.3.3 AI Solution7.4 Ai-As-A-Service7.4.1 Ai-As-A-Service Segment is Expected to Grow at the Highest CAGR during the Forecast Period7.5 Services7.5.1 Increasing Requirement of Online and Offline Support Services is Leading to the Growth of This Segment7.5.2 Deployment & Integration7.5.3 Support & Maintenance

8 Artificial Intelligence in Agriculture Market, by Application 8.1 Introduction8.2 Precision Farming8.2.1 Precision Farming is Expected to Hold the Largest Market Share during the Forecast Period8.2.2 Yield Monitoring8.2.3 Field Mapping8.2.4 Crop Scouting8.2.5 Weather Tracking & Forecasting8.2.6 Irrigation Management8.3 Livestock Monitoring8.3.1 Increasing Livestock Monitoring Applications is Driving the Growth of This Segment8.4 Drone Analytics8.4.1 Drone Analytics Application Expected to Grow at the Highest CAGR during the Forecast Period8.5 Agriculture Robots8.5.1 Increased Deep Learning Capabilities of Agriculture Robots is Driving the Growth of This Segment8.6 Labor Management8.6.1 Major Benefits Such As Reduced Production Costs Due to Labor Management Application is Leading to the Growth of This Segment8.7 Others8.7.1 Smart Greenhouse Management8.7.2 Soil Management8.7.2.1 Moisture Monitoring8.7.2.2 Nutrient Monitoring8.7.3 Fish Farming Management

9 Geographic Analysis 9.1 Introduction9.2 Americas9.2.1 North America9.2.1.1 Us9.2.1.1.1 Us Projected to Account for the Largest Size of the AI in Agriculture Market in North America9.2.1.2 Canada9.2.1.2.1 Increasing AI Technology Adoption is Leading to the Growth of Canadian AI in Agriculture Market9.2.1.3 Mexico9.2.1.3.1 AI in Agriculture Market in Mexico is Projected to Grow at the Highest CAGR during the Forecast Period9.2.2 South America9.2.2.1 Brazil9.2.2.1.1 Brazil Expected to Hold the Largest Share in the South American AI in Agriculture Market9.2.2.2 Argentina9.2.2.2.1 Expanding Industrial Production in Argentina is Driving the Market9.2.2.3 Rest of South America9.3 Europe9.3.1 Uk9.3.1.1 Increasing Adoption of Ai-Based Solutions for Agriculture is Driving the Uk Market9.3.2 Germany9.3.2.1 Germany Held the Largest Share of European AI in Agriculture Market in 20199.3.3 France9.3.3.1 Increasing Number of Start-Ups Developing AI Solutions for Agriculture is Driving the Market in Europe9.3.4 Italy9.3.4.1 AI in Agriculture Market in Italy is Growing Steadily to Overcome Drastic Climate Conditions9.3.5 Spain9.3.5.1 Favorable Government Policies are Driving the AI in Agriculture Market in Spain9.3.6 Rest of Europe9.4 Asia Pacific9.4.1 Australia9.4.1.1 Australia Expected to Hold the Largest Share of the AI in Agriculture Market in Apac9.4.2 China9.4.2.1 Increasing Precision Farming Applications in China is Expected to Drive the AI in Agriculture Market for Apac9.4.3 Japan9.4.3.1 in 2019, Japan Held the Second-Largest Share of AI in Agriculture Market in Apac9.4.4 South Korea9.4.4.1 Government Funding and Initiatives are Driving the Growth of AI in Agriculture Market in South Korea9.4.5 India9.4.5.1 India is Expected to be the Fastest-Growing AI in Agriculture Market in Apac9.4.6 Rest of Apac9.5 Rest of the World9.5.1 Increasing Awareness Among Farmers Regarding the Benefits of AI-Assisted Agricultural Operations is Driving the Market in Row

10 Competitive Landscape 10.1 Overview10.2 Ranking Analysis10.3 Competitive Scenario10.3.1 Product Launches and Developments10.3.2 Partnerships, Agreements, and Collaborations10.3.3 Mergers and Acquisitions10.4 Competitive Leadership Mapping10.4.1 Visionary Leaders10.4.2 Dynamic Differentiators10.4.3 Innovators10.4.4 Emerging Companies

11 Company Profiles 11.1 Key Players11.1.1 IBM11.1.2 Deere & Company11.1.3 Microsoft11.1.4 the Climate Corporation11.1.5 Farmers Edge11.1.6 Granular11.1.7 Ageagle11.1.8 Descartes Labs11.1.9 Prospera11.1.10 Taranis11.1.11 Awhere11.2 Right-To-Win11.3 Other Key Companies11.3.1 Gamaya11.3.2 Ec2Ce11.3.3 Precision Hawk11.3.4 Vineview11.3.5 Cainthus11.3.6 Tule Technologies11.3.7 Resson11.3.8 Connecterra11.3.9 Vision Robotics11.3.10 Farmbot11.3.11 Harvest Croo11.3.12 Peat11.3.13 Autonomous Tractor Corporation11.3.14 Trace Genomics11.3.15 Cropx Technologies

For more information about this report visit https://www.researchandmarkets.com/r/sqhb4s

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

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Global Artificial Intelligence in Agriculture Industry (2020 to 2026) - Developing Countries to Offer Significant Growth Opportunities - GlobeNewswire

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How is Artificial Intelligence Disrupting the Media and Entertainment Sector? | Head to Quantzig’s Recent Article for Comprehensive Insights -…

May 6, 2020 | Author: admin

LONDON--(BUSINESS WIRE)--Quantzig, a global data analytics and advisory firm, that delivers actionable analytics solutions to resolve complex business problems has announced the completion of its latest article that explains how artificial intelligence is transforming the media and entertainment industry.

AI and analytics is crucial from a business perspective and plays a pivotal role in driving outcomes in the media and entertainment industry. Request a FREE proposal to learn more about the business benefits of predictive analytics.

Amid the rising demand for OTT and online streaming services, media and entertainment companies face multiple challenges that can be attributed to factors such as demand fluctuations, unpredictable traffic, and personalization of services. Companies in the media and entertainment sector are investing a significant portion of their budget to improve bandwidth for streaming content seamlessly, unaware of the benefits they can obtain by using artificial intelligence to personalize user experience and search optimization while improving content-creation and production processes. Additionally, media companies can leverage artificial intelligence to automate operations and drive decision-making. In this article, we have highlighted a few benefits of artificial intelligence in the media and entertainment industry that can help companies to gather data at scale and improve the consumer experience.

Talk to our analytics experts for comprehensive insights on how our analytics can help you navigate the crisis by making better, well-informed business decisions.

According to Quantzigs advanced analytics experts, Artificial intelligence enables media and entertainment companies to precisely target audiences based on their media consumption patterns, increasing the chance of a conversion.

Benefits of Leveraging Artificial Intelligence in the Media and Entertainment Sector

At Quantzig, we understand the challenges faced by media service providers amid the crisis. To help them emerge successfully, our advanced analytics experts analyze the role of AI and analytics in driving better outcomes by shedding light on its benefits:

Book a FREE solution demo to gain limited-time complimentary access to our AI-driven analytics platforms and learn how we can help you find high-impact opportunities to differentiate yourself.

Why choose Quantzig as your advanced analytics solution provider?

With business needs changing dynamically and customer demands evolving rapidly, media service providers must focus on improving business efficiency to stay afloat. But its crucial to note that transformation is an ongoing process that deserves more profound perception and widespread adoption of advanced technology and analytics. At Quantzig, we understand the business needs of our clients which is why curated a comprehensive portfolio of AI-backed advanced analytics solutions for the media and entertainment sector to help transform business processes & ensure business continuity amid the crisis. Learn more.

We now offer a customized portfolio of business support solutions to help businesses like you navigate the COVID-19 crisis. Heres more: https://bit.ly/3b6C46P

About Quantzig

Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. For more than 15 years, we have assisted our clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent decision making. Today, our firm consists of 120+ clients, including 45 Fortune 500 companies. For more information on our engagement policies and pricing plans, visit: https://www.quantzig.com/request-for-proposal

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How is Artificial Intelligence Disrupting the Media and Entertainment Sector? | Head to Quantzig's Recent Article for Comprehensive Insights -...

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Intuality Inc.’s Artificial Intelligence Making Accurate Predictions of Coronavirus Cases and Deaths – PRNewswire

May 6, 2020 | Author: admin

WINSTON SALEM, N.C., May 5, 2020 /PRNewswire/ --Grant Renier, Chairman of IntualityInc., and Dr. Howard Rankin have been presenting, during weekly YouTube podcasts, the results of the system's cases and deaths for each of 120 days into the future since March, for the USA, Canada, UK, and 5 major EU countries.IntualityAI is tracking and predicting in real-time 500+ countries and governmental districts worldwide, as a free public service during this world-wide crisis.

"The numbers have been pretty accurate so far," says Grant Renier.So, what does IntualityAI predict about the future?

"We see a slight flattening of the curve by early July, but a second spike appearing in August. The system predicts the cumulative number of deaths in the US up to 103,000 by August 24," Grant continued.

Similar patterns are charted for the UK and Canada. By August 24, the system predicts a cumulative total of 6,800 deaths in Canada, and slightly over 38,000 deaths in the UK.

IntualityAI, the behavioral economics-based technology, has had success in forecasting in money markets, elections, sports, health and technology applications. It is the product of more than 30 years of research and development.

Dr. Howard Rankin, an expert in cognitive bias and author of "I Think Therefore I Am Wrong: A Guide to Bias, Political Correctness, Fake News and the Future of Mankind," along with Mr. Renier, hasbeen running IntualityAI podcasts related to COVID-19 atleast once a week.Accessthem on YouTube under"IntualityAI"and onitswebsite at http://www.intualityai.com.

Contact: GrantRenierPhone: 207.370.1330Email: [emailprotected]

Alt Contact: Dr. Howard RankinPhone: 843.247.2980Email: [emailprotected]

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intualityai-covid-19-prediction.png IntualityAI COVID-19 Prediction Accuracy AI prediction engine continues to predict daily COVID-19 cases and deaths within 2% of actual, since April 10, 2020.

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Company website

SOURCE Intuality Inc

IntualityAI

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Intuality Inc.'s Artificial Intelligence Making Accurate Predictions of Coronavirus Cases and Deaths - PRNewswire

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The Impending Artificial Intelligence Revolution in Healthcare – Op-Ed – HIT Consultant

May 6, 2020 | Author: admin

Harjinder Sandhu, CEO of Saykara

For at least a decade, healthcare luminaries have been predicting the coming AI revolution. In other fields, AI has evolved beyond the hype and has begun to showcase real and transformative applications: autonomous vehicles, fraud detection, personalized shopping, virtual assistants, and so on. The list is long and impressive. But in healthcare, despite the expectations and the tremendous potential in improving the delivery of care, the AI revolution is just getting started. There have been definite advancements in areas such as diagnostic imaging, logistics within healthcare, and speech recognition for documentation. Still, the realm of AI technologies that impact the cost and quality of patient care continues to be rather narrow today.

Why has AI been slow in delivering change in the care processes of healthcare? With a wealth of new AI algorithms and computing power ready to take on new challenges, the limiting function in AIs successful application has been the availability of meaningful data sets to train on. This is surprising to many, given that EHRs were supposed to have solved the data barrier.

The promise of EHRs was that they would create a wealth of actionable data that could be leveraged for better patient care. Unfortunately, this promise never fully materialized. Most of the interesting information that can be captured in the course of patient care either is not or is captured minimally or inconsistently. Often, just enough information is recorded in the EHR to support billing and is in plain text (not actionable) form. Worse, documentation requirements have had a serious impact on physicians, to whom it ultimately fell to input much of that data. Burnout and job dissatisfaction among physicians have become endemic.

EHRs didnt create the documentation challenge. But using an EHR in the exam room can significantly detract from patient care. Speech recognition has come a long way since then, although it hasnt changed that fundamental dynamic of the screen interaction that takes away from the patient. Indeed, using speech recognition, physicians stare at the screen even more intently as they must be mindful of mistakes that the speech recognition system may generate.

Having been involved in the advancement of speech recognition in the healthcare domain and been witness to its successes and failures, I continue to believe that the next stage in the evolution of this technology would be to free physicians from the tyranny of the screen. To evolve from speech recognition systems to AI-based virtual scribes that listen to doctor-patient conversations, creating notes, and entering orders.

Using a human scribe solves a significant part of the problem for physicians scribes relieve the physician of having to enter data manually. For many physicians, a scribe has allowed them to reclaim their work lives (they can focus on patients rather than computers) as well as their personal lives (fewer evening hours completing patient notes). However, the inherent cost of both training and then employing a scribe has led to many efforts to build digital counterparts, AI-based scribes that can replicate the work of a human scribe.

Building an AI scribe is hard. It requires a substantially more sophisticated system than the current generation of speech recognition systems. Interpreting natural language conversation is one of the next major frontiers for AI in any domain. The current generation of virtual assistants, like Alexa and Siri, simplify the challenge by putting boundaries on speech, forcing a user, for example, to express a single idea at a time, within a few seconds and within the boundaries of a list of skills that these systems know how to interpret.

In contrast, an AI system that is listening to doctor-patient conversations must deal with the complexity of human speech and narrative. A patient visit could last five minutes or an hour, the speech involves at least two parties (the doctor and the patient), and a patients visit can meander to irrelevant details and branches that dont necessarily contribute to a physician making their diagnosis.

As a result of the complexity of conversational speech, it is still quite early for fully autonomous AI scribes. In the meantime, augmented AI scribes, AI systems augmented by human power, are filling in the gaps of AI competency and allowing these systems to succeed while incrementally chipping away at the goal of making these systems fully autonomous. These systems are beginning to do more than simply relieve doctors of the burden of documentation, though that is obviously important. The real transformative impact will be from capturing a comprehensive set of data about a patient journey in a structured and consistent fashion and putting that into the medical records, thereby building a base for all other AI applications to come.

About Harjinder Sandhu

Harjinder Sandhu, CEO of Saykara, a company leveraging the power and simplicity of the human voice to make delivering great care easier while streamlining physician workflow

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The Impending Artificial Intelligence Revolution in Healthcare - Op-Ed - HIT Consultant

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