Though the use of machine learning increased extensively in the previous years because of its excellent applications that altered the world, the idea has been prevalent for over half a century. This term was introduced by A.L. Samuel, who asserted that Machine Learning refers to the idea of machine learning on its own in the absence of any kind of programming. This has brought about a significant increase in its efficiency and made it one of the best human inventions across the world. In the present times, every minor thing that humans don’t observe intently is related to artificial intelligence and its relevant fields, which has become deeply embedded within society.
Before a detailed discussion on machine learning is carried out, I would like to share a short story. Long time back, a checkers program was created by IBM that was self-learning. This program was able to recognize the good and bad aspects of the existing game by evaluating various games. Because of this, there were improvements in the game, which rapidly outperformed the popular Samuel’s Checker Player. He was called in 1956 by John Johan McCarthy, the founder of artificial intelligence and the recipient of the Turing Award 1971 to put forward his work at the Dartmouth Conference. This was the starting point for the discipline of artificial intelligence. Samuel ensured that the significance of machine learning was comprehended by everyone, as well as its extensive uses that can bring about vast developments in society.
The study of Samuel on his experience with machine learning was issued in IBM Journal in 1959, after which Edward Feigenbaum, who was called the father of Knowledge Engineering and who received the Turing Award in 1994, published his magnum opus Compilers. In 1961, an invitation was put forward to Samuel to present an example of the finest game within the program. Taking up this opportunity, Samuel made a challenge to the checker’s champion of Connecticut, who was the fourth-best player in the United States at that time. The program was successful and this created an uproar in the tech industry and made people realize the significance of artificial intelligence.
The impact of Samuel’s checkers’ program was not only profound on artificial intelligence, but also on the advancement of the overall field of computer science. It was indicated by the preliminary research in computer science that tasks that were not clearly programmed beforehand could not be carried out by computers. This hypothesis was rejected by the checkers’ program of Samuel. In addition, this program was also one of the earliest to execute non-numerical computing tasks on a computer. The structured instruction design of this program had a significant impact on the instruction set of IBM Computer, and subsequently, other computer designers also incorporated this design.
Development of Machine Learning in Detail
The advancements in Machine Learning occurred quite rapidly due to artificial intelligence research that occurred in collaboration with professors and scientists from all over the world. When the studies on artificial intelligence were in its reasoning stage in the 1950s and early part of 1970s, it was presumed that if provided the ability to perform logical reasoning, machines may be intelligent. In this stage, the representative work essentially comprised of the Logic Theorist program of A. Newell and H. Simon’s, and subsequently, the General Problem Solver Program.
In addition, various other programs were developed that attained promising outcomes at that time. For instance, 38 theorems in the eminent mathematician Russell and Alfred North Whitehead’s Principal Mathematics in 1952 and all the 52 theorems in 1963 were proved by the logical theoretician program. As research progressed, it was eventually accepted by people that only logical reasoning is not sufficient to attain artificial intelligence. Rather, it was asserted by E.A. Feigenbaum and others that a machine could become intelligent when it had a large amount of information and the ability to think like humans.
From the mid-1970s, artificial intelligence research, under the supervision of these authors, entered the knowledge period. Several expert systems were established in this period and these were applied to attain significant outcomes. The Turing Award was presented to E.A. Feigenbaum in 1994 in recognition of his efforts in knowledge engineering. Nonetheless, it was soon accepted by people that Knowledge Engineering Bottleneck was being experienced by the expert system. To sum it up, humans face several issues in collecting knowledge and then transferring it to the computer. Hence, certain scholars were of the view that positive outcomes would be attained if machines were capable of learning themselves, which would also enable computers to think in the same way as humans.
Definition of Machine Learning
In simple language, a computer improves its performance on tasks as it gains experience. The definitions provided by the leaders and scholars of machine learning allows us to present a succinct explanation of machine learning that learns on its own without receiving logical instructions. However, it uses substantial data through which machines are able to think in the same manner as humans.
It can be deduced from this description of machine learning that the most significant elements of Machine Learning are:
Data: Eventually, the experience is converted into data that can be comprehended by a computer so that it can learn from this data. The machine that has the highest amount of data and the best quality exhibits the greatest leverage in machine learning and artificial intelligence. In human terms, data is similar to the educational context, and a critical aspect of being smart is being capable of acquiring a good education. Therefore, it is evident that an internet company, such as Google, may be able to formulate machine learning programs that exhibit excellent performance because they have acquired a significant quantity of data that they have obtained from their users.
Model: The key critical source or path for a machine learning application is the Algorithm. Using data, we can develop a perfect model, as was required. Once infinite data is used to train the model, positive outcomes will be attained, signified by the enhancement in the machine. Once training has been done with significant amounts of data, the model turns into the core of machine learning, due to which the model becomes central to decision making. A relevant response will be made by a well-trained model when a new event is being inserted, which will provide high-quality output.
Explaining Machine Learning with Daily Life Examples
The usage of machine learning became quite widespread half a century following its birth because of Moore’s law, which states that as the performance of computer increases, the cost of computing resources decreases. It may not be evident, but artificial intelligence, deep learning-based applications and machine learning have become a vital part of your routine life.
A detailed example of the way machine learning has become embedded in our lives will now be presented, which will help you in understanding the topic and its scope in greater detail.
The first thing you do when you get up in the morning is to switch on Siri on your iPhone or Google Assistant in your Android or Cortana on your Windows laptop to ask the weather for the day. The city will be automatically located by Siri, and its weather information will be presented. It is easy to use this function; however, an intricate system is present behind this function. Speech recognition is one of the foremost uses of machine learning, in which your speech is converted into words by Siri. Speech is basically a series of waves that have distinct amplitudes.
A model needs to be developed to convert speech into text. The model first needs to be trained using a large amount of speech input. After training the model, speech is considered as the input, and the information can be additionally processed by the input from different sources to provide you the information you require. The studies on speech recognition commenced in the 1950s, which continued to develop with the progression of time. The model now employs neural networks and other deep learning methods to attain improved outcomes.
Natural language process is another vital element of machine learning and artificial intelligence. Once text is converted by Siri into voice, the words need to be comprehended by Siri to provide a precise response. Words cannot be understood easily by a computer. There needs to be a wide-scale Corpus, after which, a relevant language model should be developed. Next, using the Corpus, the language model should be trained such that it can ultimately comprehend portions of the text semantics. To process natural language and use search engine technology, refer to books that are exciting to read and make it easy to comprehend Advanced Math.
Coming back to the story, while getting ready, you check your phone for news updates. You notice an advertisement for a camera underneath the news section. You remember that just yesterday, you spoke to your friend about purchasing a camera. This was automatically understood by your phone, which suggested an advertisement for a camera you may be interested in buying. You click on it, leading to a popular e-commerce website. You check the price and read the reviews, after which you decide to purchase a less costly product. After going over the news for some time, you realize that the news seems to have a human element in it, as it automatically invites you to read your favorite news by presenting it on the front page.
Finally, when you are on your way to work, you catch a seat on the subway, switch on your music player and have a look at your music library in Spotify. You don’t find the songs you want to listen to, so you make a request to the system to suggest a few songs. You find the system that suggested the songs quite reliable, as you have never heard the songs it suggested before but like them the first time around.
The recommender system in the example given above is a significant use of machine learning. The basis of the recommender system is to learn the usage habits of the user and then create their picture. Then using this picture, the system suggests songs, products, articles, and any other piece of information that may be appealing to the user.
Once you reach your workplace, you are greeted with a latest face recognition system that allows you to enter the office premises after identifying your face. Machine learning algorithms are used by this face recognition system to identify your face with varied expressions. The best part of the system is that it automatically detects your face and then welcomes you by wishing you according to the time of the day. Currently, near all the sophisticated face recognition systems are founded on the basis of the algorithm of the deep learning model that employs intricate patterns to identify faces with precision.
Data Mining
Presently, data mining is used overtly as a statistical technique in a negative sense as conventional statistical research usually depends on the beauty of the theory instead of its functionality. However, this has changed in the recent times because a greater number of statisticians are now concentrating on issues that are related to some extent with shared experiences encountered by individuals when using machine learning.
When the fundamental idea of scientific research shifts from the “theory + experiment” approach of the past to the “theory + experiment + calculation” of the present times, and the term “data science” comes into being, there is an increase in the significance of machine learning because the objective of computation is data analysis. The basic idea of data science is to analyze data to increase value. Machine learning (ML) belongs to the list of most active and prominent areas of research in the fields of computer science and technology.
Why does Machine Learning Matters with reference to Data Mining?
The first Machine Learning Department across the globe was introduced by the Carnegie Mellon University, one of the pioneers in this field, in 2006. Interest was subsequently shown by American presidential group in advancing machine learning, and Professor Mitchell was made the first departmental head of this field. A rigorous program was introduced by the National Science Foundation at the University of California, Berkeley. Three main technologies for extensive research and integration in the big data period were stressed in this program, i.e. cloud computing, machine learning and crowd sourcing. The critical fundamental technology of the big data period is machine learning. This is because the objective of gathering, storing, transmitting, and handling big data is to “leverage” it, and in the absence of machine learning to analyze this data, it cannot be used in any way.
It was in the 1990s that the data mining field emerged. Several fields had an impact on it, with the most influential ones being machine learning, database and statistics. Data mining refers to the extraction of knowledge from a large amount of data, including the management and analysis of this data. Research in the database typically offers data management methods for data mining, whereas the studies on machine learning and statistics offer data analysis approaches for data mining.
This shows that data mining is essentially affected by statistics through machine learning; the two key supports of data mining are the machine learning domain and the database domain. This shows the profound impact of statistics on data mining using machine learning.
In the present times, the lives of normal people are affected by machine learning. For instance, when machine learning techniques are used correctly in evaluating data from satellites and sensors in the fields of energy exploration, weather prediction and environmental supervision, forecasting and detection carried out is more accurate. Machine learning can be used to perform extensive research of sales data, and customer information not only assists business in optimizing inventory and decreasing costs, but also in developing pertinent marketing strategies for their user base. Some more examples are presented below.
Changes have been brought about in the human life by Google, Bing and other search engines over the web. For instance, prior to travelling, many people search the internet to obtain more information about the destination and to identify the best places to visit.
I will present another example now. Car accidents are a major cause of deaths of humans all over the world, causing deaths of one million people a year globally and almost 100,000 in our country. A key solution to this problem is to have computers drive an autonomous car as machines on the roads would be able to avoid problems caused by inexperienced drivers, drunk driving and fatigue driving. In addition, it also has significant military uses.
Studies have been carried out in this field in the United States since 1980s. The greatest issue faced here is that it is not possible to consider the design and program of every situation that the car may experience on the road within a car factory. Therefore, the information that the onboard sensors receive can be considered as the input, and the control behavior of brake, direction and throttle can be considered as the output, depending on the situation experienced on the road. Thereafter, the main issue here can be conceptualized as a machine learning task.
Machine learning subsequently turned into the cornerstone of the Internet search. In the present times, the complexity of topics and contents of the search is increasing, and the effect of machine learning technology is becoming more evident. For instance, Google and Bing both carry out “image search” using the latest machine learning technology. Research teams that are experienced in machine learning technology have been established by Google, Facebook, Bing, Yahoo and other search companies.
Even the social and political lives of humans have been affected by machine learning technology. A machine learning team was set up by Obama during the 2012 election, which performed analysis of the election data to tell him what the future course of action should be. For instance, machine learning was used to evaluate social network data, identify the voters that would turn following the foremost debate of a presidential candidate and formulate personalized communication strategies on the basis of the analytical findings. Using machine learning models, Google was able to determine the most convincing factors that make voters stay.
The reason why a lot of attention has been awarded to machine learning is that it is the basis on which innovation in intelligent data analytics has occurred. There is another vital element of machine learning research that should be taken into account, which is to improve our understanding of the way humans learn by developing computational models of learning. For instance, when Sparse Distributed Memory model was put forward by P. Kanerva (Kanerva, 1988) in the 1980s, it intentionally avoided imitating the physiological constitution of the brain.
However, it was subsequently determined in neuroscience research that the scarce encoding procedure of SDM is extensively prevalent in the cortex of visual, auditory and olfactory functions. In short, it can be asserted that natural science research is driven by the curiosity of humans regarding the origin of the universe, the nature of life, the crux of everything and self-consciousness. Machine learning is quite successful in natural and biomedical sciences, in addition to all other fields of study that can be learned with experience.
Types of Machine Learning
Following extensive research and experimentation, it was decided by the computer scientists to classify machine learning into two groups to attain an improved understanding and perform more rapid research. The two groups are discussed below:
This is the process in which laws are learned by a computer using several known input-output pairs of data, which enables it to present logical output predictions for fresh input. There are distinct features in the current data (location, area, developer, orientation, etc.) with respect to the house cost data. Once this data is learned, it becomes possible to determine the cost of a known feature of the house. This technique is referred to as Regression learning, where the output is a particular value and its prediction model signifies a continuous function.
Also consider the situation when there are several emails, all of which have been tagged as spam. A model is created by examining the emails that have been tagged, and this model is able to determine precisely whether a new mail is a spam or not. This is referred to as Classification learning, where a discrete output is obtained. The output 1 is either spam, or output 0 is not spam.
In implicit terms, this refers to learning from undifferentiated data to comprehend its intrinsic features and structure. There is widespread access to customer data with corporate firms like Amazon, who use this data to analyze the various groups of customers that shop with them. The question asked is the number of categories we would create from this data and the distinct attributes of each category. This is referred to as clustering. The categories need to be differentiated from one another in supervised learning, where the categories are already known.
On the other hand, clustering in the process in which we are not aware of the different categories that are present till data analysis has been carried out. This means that the classification problem is to select one of the known responses, while the response to the clustering problem is not known and an algorithm is required to identify the structure and features of the data.
The major difference among the two kinds of machine learning is that when data is trained with supervised learning, known outcomes to monitor are obtained, whereas when data is trained with unsupervised learning, no known outcomes are obtained for monitoring.
Explaining Machine Learning Concepts with a House Price Evaluation System
In this section, the concepts of machine learning are explained in a straightforward manner by presenting simple examples that can be related by anyone to their practical life. For example, if we wish to formulate a house price evaluation mechanism, a known feature of the house price evaluation forecast is the objective of the system. The steps given below are followed when developing this kind of system:
Training Samples
Several varied features of the house are required, and information regarding their costs. This information can be directly acquired from the Property Assessment Center, including information regarding the house, for example, the size of the house, its geographical placement, price, orientation, etc. Further information may also be required, which may or not be available at a real estate appraisal centre, like a school close to the house, a feature that frequently has an impact on the cost of the house.
Data would not need to be gathered from other sources. These data are referred to as training samples or data sets. The term features are used to represent the location, size and other characteristics of a house. In the stage of data collection, it is important to obtain information on various features. The extent to which the feature set is complete determines the amount of data gathered and the precision of the trained model, and this would offer more precise results.
This process also helps in determining the cost of collecting data. The transactions carried out in the past from different sources can be analyzed using machine learning, which then provides a credit score on the basis of your payments and debts. This credit score is vital for banks as it makes them aware of possible risk of fraud, leading to more transparent financial transactions.
Data Tagging
With respect to the home appraisal system, we receive the home price information from the home appraisal center, but this information may not be accurate. At times, the appraisal cost of the house is significantly less than the actual transaction price of the house so as to evade taxes. Here, a process known as data tagging is used to obtain the actual transaction cost of the house. There can be artificial markers, for example, asking the actual cost of a house one after the other from a real estate agent, or the process can be automated, like analyzing data by reaching out to the Real Estate Assessment Center to determine the cost of the house and the actual cost of the match, and then using different complex algorithms to directly compute this value.
Data tagging is a critical part of supervised learning methods. For instance, for a spam filtering system, flagged data should be part of our training sample which shows if a message is a spam or not. A critical part is played by data for machine learning. In case of insufficient or unreliable data, illogical findings will be obtained, which will be very different from reality.
Data Cleansing
Consider the situation where data is obtained on the size of the house, which is frequently represented using square meters. This data has to be collected and implemented in your house, a process referred to as data cleansing. In this process, duplicate and noisy data is also eliminated, providing it a structured character which can be used easily as input for machine learning algorithms. If a hundred house features are obtained, and these features are examined one after the other, following which a total of thirty features are chosen as input. This process is referred to as feature selection. One of the ways in which features selection is carried out is through manual selection, where people examine every feature and then choose the most relevant feature set. The other approach is to use models to perform the selection automatically, for example, the PCA Algorithm.
This example is related to the foremost category of machine learning that addresses the simplest linear equation to simulate. The choice of the model depends on the problem domain, training time, data volume, model precision, and a few other factors.
Training Data Sets
Data sets are typically of two kinds, where one is involved in training and the other pertains to testing. The two sets are typically distributed in a ratio of 8:2 or 7:3. The training data sets can then be improvised and applied in a way that they improve the efficacy of the model. Following the training, tests can be carried out to determine how accurate and reliable the model is. The reason why separate test data set should be tested is to ensure that the test results are accurate, meaning that the model is tested using data that is not known to it from beforehand, and not with the data that was used previously while training the model.
Theoretically, a dataset is divided more logically by classifying it into three, in addition to a cross-validation dataset. Once the model is developed, a process needs to be established that allows us to assess various conditions and perform a comprehensive assessment of the performance. There are several aspects of performance evaluation, as explained below.
Training duration refers to the time taken for training the model. It may take almost a month or even more to train a model for certain big data machine learning applications, and at this point, the efficiency of the algorithm becomes critical. It is also very important to determine if a sufficient number of data sets are available. In general, the system with intricate features receives more benefits when the training data set is larger. It is also imperative to determine the precision of the model, and if new data can be predicted correctly. Lastly, it needs to be determined if the model is able to fulfill the performance needs within the context of the application. If it is unable to fulfill the requirements, then the model needs to be adjusted, after which the model should be trained further, as well as examined or shifted to other models.
The parameters can be saved by the trained model and inserted directly the next time it is used. In general, a significant amount of computations is required by the model training, which takes a long time to be trained as it is important to train a good model parameter over a large data set. Nonetheless, the computational load is comparatively less when the model is employed. In general, the new sample functions as the input, and the model is run to obtain the prediction outcome.
Scope of Machine Learning
The idea of Industry 4.0 strategy was first put forward in April 2013, a day that was quite memorable in the history of machine learning. Industry 4.0 signifies the intelligent manufacturing-oriented information revolution or revolutionary production techniques. The purpose of the strategy is to accomplish the objectives of the Millennium Developing using rapid technological development through various machine learning, artificial intelligence and deep learning methods.
When a virtual system is integrated with information physics system, there will be a transformation of the manufacturing industry into intelligence. On the basis of the prospective trends in intelligence technologies like machine learning, 4.0 is an upcoming trend that comprises of different development technologies such as intelligent systems, Pentagon Resources, distributed computing, OCR recognition with machine translation, facial identification with precise prediction, and image manipulation with accuracy, in addition to the Internet of Things that will be available in the subsequent years.
The commander of the U.S. Army Medical Center, Major General Steve Jones, stated in a U.S. army conference in September 2015 that humans could be replaced by intelligent robots on the battlefield to shift the wounded. A high-profile statement has also been made by the US military that in the future, it is not the soldiers who may be required rescuing on the battlefield, instead, it would be robots because humans will soon be replaced by an army of intelligent robots.
The concept of a “Big Bang” for artificial intelligence seemed to be a dream for science fiction authors till the 21st century. However, currently, a greater number of people are starting to give serious thought to what is going to be the outcome of humanity when technology singularity is attained.
There has been exponential increase in science and technology, which is why it seems that the human defense during wartimes is impending. Google, Genentech and Autodesk established a university that has three programs pertaining to medicine, robotics, data science, biotechnology and corporate governance. A high degree of computing power is provided by cloud computing, and there is extensive use of big data algorithms, though they are not sufficient to make computers smarter. They generate the powerful AI that is required for big data to allow machines to learn from significant amounts of information, and the computing power of cloud computing is cheap and strong enough to resemble that of the human mind.
Machine learning will soon shift into the more robust AI, which will have actual intelligent machines that are able to perform logical thinking and problem solving. The level of consciousness and self-recognition of these machines will be identical to the intelligence level of humans.
Applications of Machine Learning
There is extensive application of machine learning; machine learning algorithms have the potential to be employed in military as well as civilian settings.
Data Analysis and Mining
Data mining and data analysis are two terms that are considered to have the same meaning and are often used interchangeably. In data analysis, relevant statistical tools are used to obtain extensive primary source and secondary information analysis. It refers to the collection of valuable information and making conclusions on the data that can be examined in detail subsequently. The purpose of both data analysis and data mining is to facilitate the collection and analysis of data, convert it into information and then come up with conclusions.
Data analysis and mining technology are brought about by machine learning algorithm and data access technology by performing knowledge identification, statistical analysis and other methods offered by machine learning to evaluate large numbers. Similarly, to achieve competent data reading and writing, the data access method is utilized. It is not possible to replace machine learning in data analysis and mining, and this was enhanced by the inclusion of Hadoop into machine learning in 2012.
Myrrix was purchased by Cloudera in 2012 to co-found Big Learning, after which the machine learning club formed a partnership with Hadoop. Because of cheap hardware, it has become easier to perform big data analytics. In addition, due to the decrease in the costs of hard drivers and CPUS, and developments in open-source databases and computing models, it is possible for startups as well as individuals to carry out terabytes of complicated computing. The Apache Mahout project gave rise to Myrrix, which is a real-time, scalable clustering and recommendation system that uses on machine learning.
Machine learning is also being used by other big companies to evaluate date so that the quality of their products and services can be enhanced. The Azure Machine learning system was formally introduced by Microsoft and is already running on Xbox and Bing, with support for Python, R, Spark, Hadoop, and various other frameworks. AI is used by Ford to perform its work scheduling and come up with solutions for its scheduling problems that are experienced because of an increasing number of employees. Deep learning from neural networks is employed by a few companies to perform virtual drug screening with the aim of substituting or enhancing the computational techniques employed in high throughput screening. Machine learning algorithms were employed by Yahoo to extract data from 16 billion emails, and 16 billion emails among 2 million people were analyzed by researchers in the lab.
To Analyze User Behavior
Machine learning is used by PayPal to prevent fraud. Fraud is identified using machine learning and statistical models, and more advanced algorithms are used to filter transactions. The machine learning service has been provided by AWS to European developers and it can be used across the Dublin AWS region. It is anticipated by the company that Amazon’s machine learning will help resolve the limitation issue and that the analysis and prediction will be carried out with data from Europe, without leaving the region.
Pattern Recognition
Pattern recognition has undergone modifications and developments because of the integration of pattern recognition that emerged in the field of engineering and machine learning that emerged from computer science. There are essentially two areas that are the focus of pattern recognition research; the first is to examine how objectives are viewed by organisms (including humans), which is part of the domain of cognitive science. The other area is the way computers are to be used in certain tasks.
One of the most innovative uses of machine learning is pattern recognition, which can be used to identify images, fonts, texts, calligraphy and much more that cannot possibly be detected in any other way by humans. In addition, there are improvements in the systems on a daily basis, and it is expected that by 2050, they would be able to comprehend any pattern. Various fields make use of pattern recognition, such as medical image analysis, computer vision, optical character recognition, speech recognition, natural language processing, biometrics, and handwriting recognition. There are other fields in which machine learning is deep-rooted, for example, search engines and file classification.
Wider Areas
The year 2015 can be considered as the year of machine learning, and it is now time to bring about a revolution in this field so as to bring changes in human society. It is not just the large companies like Google, Amazon, Toyota, Accenture, Johnson & Johnson and Tesla that have incorporate machine learning on a wide scale; rather, the startups are also increasingly adopting artificial intelligence to achieve more rapid progress. The machine learning revolution is at the same level as the big companies. Innovative applications for machine learning have already been introduced by startups, and investors are giving attention to these startups. AI has been incorporated by over 170 startups, while significant investments have been made in AI by the bigger companies, like IBM and Google.
Using machine learning in every aspect of life all over the globe is at its peak. Computers have effectively been converted by Google DeepMind researchers into experts of Atari Video Games that are using machine learning. A partnership has been established between Google and Johnson & Johnson to develop AI surgical robots that can provide assistance to surgeons to decrease the risk of injuries among patients.
GraphLab is one of the latest machine learning platforms. It is an open-source project that was renamed Dato and managed to raise funds worth $18.5 million. The purpose of developing this platform was to enable machines to perform analysis of images. Chat was developed by Microsoft for Microsoft Cortana, and ML allowed Cortana to not just identify jokes and make predictions of sports matches, but also make a suggestion is to reach meetings early (to take into account the traffic delays).
The 18-core Xeon chip was Intel was adjusted in accordance with machine learning and was designed to fulfil the needs of the rapidly evolving services market. According to the company, this chip works six times faster as compared to the new chip. Aerosolve has been introduced by Airbnb, which is a machine learning package that will supposedly function better when humans and machines work together rather than when they work independently. Gartner’s 2015 Hype Cycle report also featured machine learning, which only presents digital technologies that it believes to have a profound effect, and the first technology to be included in the report is humanism.
Read: Step by Step Guide to Develop AI and ML Projects for Business
