Introduction to Artificial Intelligence & Machine Learning
Introduction to Artificial Intelligence & Machine Learning These days, AI is a hot topic. So let’s delve into where the field of artificial intelligence originated and is going.
The first research into artificial intelligence to have a lasting influence began in the early to mid-20th century. From there, research persevered, at times facing significant headwinds, until reaching the point where we are at today.
The Beginnings : Introduction to Artificial Intelligence & Machine Learning
The complex field saw its humble beginnings as ideas in the mind of early pioneers like Alan Turing. Turing was a British mathematician who wrote a groundbreaking paper, proposing a concept called the Turing Machine. A Turing Machine is a concept of one that can perform operations on a (potentially conceptual) piece of tape by reading and printing out values. Turing machines should be able to execute basic algorithms. Today, practically all common programming languages (Java, Python, C++, etc.) fulfill these tasks. And are called “Turing-complete.”
Turing made more developments in 1950, when he came up with the Turing Test, a way of defining computational intelligence. According to Turing, a machine was intelligent if and only if it could fool a suspicious judge into believing it was a human in a written conversation with an actual person. To this day, researchers and engineers have struggled to pass the Turing Test . As it turns out, what comes naturally for human beings is actually a great hardship for computational machines.
In 1956, artificial intelligence received a lot of attention. This was thanks to the 1956 Dartmouth Summer Research Project on Artificial Intelligence. Which brought together top minds to theorize and speculate on the feasibility and forms of AI. Though not much of significant material substance was produced during the conference, artificial intelligence was deemed achievable, the spotlight was shone on the field, and more focused research began in the years to come .
After the Dartmouth conference
Algorithms improved processing power. This was at first incredibly limited. But eventually Ai skyrocketed with processing power.
Initially, the United States provided significant assistance through the Defense Advanced Research Projects Agency (DARPA) .
One of the motivations for the government to back AI technology was for its use in rapid translation, specifically for Russian documents in the Cold War era.
Cold War Disappointment
However, disappointed by slower than expected progress and unimpressive return from the investments they made in the technology, the US government ceased funding AI research in 1974. This lack of funding characterized a period that was later deemed “AI Winter.” One takeaway researchers later took from the dark ages of AI is that excessive levels of hype surrounding a technology can be harmful if interest wanes too much following perceived disappointment .
Fortunately, research in AI saw a resurgence in the early 1980s. This is when “deep learning” first began to see recognition. DL allows AI models to learn from experience. And DL learns through the use of expert systems. In essence, an expert system is a clever computer program. This program can pass on knowledge from more seasoned company workers to newer ones and by doing so boost productivity.
Japan was an early proponent of expert systems. Its government gave out nearly half a billion dollars to researchers with the hopes of transforming industry in the Fifth Generation Computer Project (FGCP) from 1982 to 1990 .
A pivotal moment many have heard of in artificial intelligence is the creation and success of the Chess AI, Deep Blue . Developed by the former computing behemoth IBM, Deep Blue leveraged the brute force computation of potential chess moves to play matches at an incredibly high level. In a groundbreaking and highly publicized match in 1997, Deep Blue defeated chess grandmaster Gary Kasparov. Attention turned toward applying AI to all kinds of practical problems, games, and business applications.
Many important services, from social media to retail to marketing, are run on “big data.” Massive quantities of information are aggregated. From users and historic data, to raw processing power. Thanks to continuous improvements in computer processors! We can sift through and create inferences from the data. With the help of an abundance of information, a number of fields open up. Such as, computer vision, natural language processing, and autonomous driving are seeing significant development. The rapid expansion of AI technology and data collection has also raised ethical and moral concerns.
What happens when jobs involving repetitive labor are replaced en masse by machines and processors? These are all issues we will need to give serious thought to and grapple with moving forward. By appreciating AI’s past, we can put today’s achievements in context and frame further, and more ethical, development in the future.
Machine Learning Introduction : Chess is well-known for its complexity that has spawned many opening theories and tactics. In the 1990s, Garry Kasparov reigned supreme in the chess world. The ordinary person would be in total amazement over his chess games. However, in 1997, the chess-playing computer Deep Blue famously defeated Kasparov. It was in a six-game series, becoming the first artificial intelligence to defeat a world champion.
Now, as computers have become more advanced, chess engines are able to find the best move in the most complex positions in less than a second. Accordingly, artificial intelligence and its numerous abilities and applications have captured the attention of every industry. Machine learning, a specific form of AI, has particularly raised eyebrows due to its rapid improvement over the past years. At Google, researchers have experimented with the use of machine learning in their search engine. But what exactly is machine learning?
Machine Learning : Introduction to Artificial Intelligence & Machine Learning
Machine Learning is a subdivision of AI. Which focuses on constructing machines that can improve upon themselves with only initial human intervention. As explained by Google at their “Machine Learning 101” event. A typical machine learning system consists of three parts: the parameters, model, and learner. The parameters are the data sets. And also known as training sets, that the machine is given. Parameters can also take the form of rules that are defined by humans.
These parameters produce a model which then makes the predictions. Typically, engineers will input initial parameters as well as a model. After producing a model, the machine uses its learner to identify discrepancies between its predictions and the actual results. In a continuous process known as gradient learning or gradient descent, the machine adjusts its model to predict patterns that are more representative of the actual results. Humans may also feed the machine more historical data sets so that the model will make more accurate predictions. Once the machine has undergone sufficient gradient learning, it is ready for use.
Supervised and Unsupervised learning
Machine learning is categorized into both supervised and unsupervised learning. Supervised learning creates functions between training data and the results. Using previous data and established input-output connections, supervised learning allows for the machine to make quick relations. On the other hand, unsupervised learning makes the machine cluster information together in order to make predictions. If a machine was given the task of separating a mixture of basketballs, baseballs, and footballs into their own groups.
Using supervised learning, a preexisting data set allows the machine to identify footballs as the brown objects. Then baseballs as the white objects, and basketballs as orange objects. The machine would use this information to sort the objects quickly. For unsupervised learning, the machine would recognize the difference between the balls on its own. It is noticing that the different balls are easily distinguishable by color. Although it may be less accurate and slightly more time-consuming. The machine is able to complete the task with less human intervention.
Basic machine learning can still have some shortcomings. When giving initial parameters to the machine, there can be difficulties articulating complex rules or providing data sets. For instance, building a machine that can identify cats from images is far more difficult than building one to analyze numbers because it is relatively hard to break down the appearance of a cat. Thankfully, with the emergence of machine learning came similar techniques such as deep learning. Deep learning is the use of artificial neural networks. DL simulates the human mind, in order to simplify data and identify patterns. Even before this decade, computers were not strong enough to make deep learning a viable option.
However, continuous technological advancements have pushed deep learning to the forefront of machine learning.
For now, most machine learning tasks can be done by humans. It requires the ability to analyze data. And identify patterns, and make the right judgments, all of which humans are capable of. What makes machine learning so interesting is its limitless potential. One day, machine learning will be able to solve issues that humans never could. As computing power inevitably outpaces the capabilities of any human mind, every industry will feel the impact of artificial intelligence.
Machine Learning Vs Artificial Intelligence : Introduction to Artificial Intelligence & Machine Learning
Background Introduction: Machine learning and artificial intelligence both are fields of computer science and are closely related to each other – Machine learning is a subfield of AI. But, intelligent systems will use both Ai & ML. However, those technologies do have some major differences which apply in computer science, big data, and many other applications. In this report, I am going to illustrate the differences between machine learning and artificial intelligence in the following several ways.
Functionalities of AI and ML: Artificial Intelligence is a field of computer science that can mimic human intelligence, it is more focusing on computer programming and algorithms. The mimic human behavior will include problem-solving, learning, logical thinking, and planning. The most famous example of the use of artificial intelligence was how the chess game program Alpha-Go beat human beings. That was also one of the concerns many people talk about today regarding the development of artificial intelligence and how they are going to deep dive into people’s daily life.
Helping Data Scientists
While machine learning enables a computer system to drive decisions or make predictions based on data-driven methods by utilizing historical data. It is more focused on a high level of mathematics and optimization. There are many different kinds of machine learning algorithms developed by data scientists and mathematicians such as logistic regression, random forest, boosted trees, etc.
All those methods are helping data scientists better implement their classification or regression models. Specifically speaking, machine learning is a subfield of artificial intelligence, it is a type of artificial intelligence.
Skillsets and career options for AI and ML: For artificial intelligence, the skill sets will involve robotics, JAVA programming, programming design, data mining, machine learning, and problem-solving. The future careers provided for students in advanced AI will be deep learning engineers, programmers, computer vision engineers. For machine learning, the skill sets will involve applied mathematics, neural network architecture, physics, data modeling, and designing.
The future careers provided for students in the advanced ML will be data scientists, machine learning engineers. Besides, since AI is the main category for machine learning, it has more applications and sub-categories such as strong AI, weak AI, general AI. Machine learning has three sub-types such as supervised learning, reinforcement learning, and unsupervised learning.
Potential of AI and ML : Introduction to Artificial Intelligence & Machine Learning
AI has drawn a lot of people’s attention in those days and many people are concerned about the safety or privacy of themselves in AI. People have a number of concerns regarding cybersecurity. Which is directly related to people’s daily life. People keep giving imaginations and illusions for the development of AI.
The application fields of Artificial Intelligence also cover healthcare, finance, real estate, database administration, and the personal device market. The use of Artificial Intelligence in those industries provides people a more convenient way of doing business while at the same time, brings people some security concerns regarding their health record, financial status, and personal information.
Machine learning also has a wide array of applications.
Advanced Machine Learning
Therefore, advanced machine learning could be self-learning and possess the ability for self-improvement. Many people worry that, once even a little human error or machine error breaks down, it will largely affect people’s lives and business running. Engineers and mathematicians have to keep improving the functionalities of machine learning models to make them have higher accuracy. In conclusion, machine learning and artificial intelligence are very closely related to each other and share a lot in common in both computer science and mathematics fields. While machine learning is a sub-category and one appearance of artificial intelligence and has different functionalities.
Machine Learning And Data Analytics : Rise Of A New Revolution In Society & Business Just before World War I, Supreme Commander of the Allies Ferdinand Foch brushed off the idea of using planes in combat, saying “[they] are interesting scientific toys, but… are of no military value.”
After World Wars I & 2, two atomic bombs, decades of aviation-centric wars and an Obama administration’s worth of drone strikes, Foch is looking a little silly.
Foch couldn’t have known how airplane technology would take off; he made an ignorant guess. He looked into a dark abyss, where something mysterious was growing, and proclaimed that it was empty.
Data mining is a powerful process that uses statistics and machine learning to find patterns in data. It gives corporations valuable information, such as the behaviors of consumers, allowing for product specialization and, consequently, incredible profits.
With great power come headstrong, greedy idiots, like Cornell Professor Brian Wansink. Once at the top of his field of food psychology, Brian pumped out article after article of fabricated trends. Researchers decided to question him on his findings from an Italian buffet when he claimed that “men eat 93 percent more pizza when they eat with women.”1
Wansink tried to use smart calculators to replace brainwork, and unfortunately, that doesn’t work in science. Maybe he should have read up on Icarus.
Ai And Machine learning The Next Step : Introduction to Artificial Intelligence & Machine Learning
As technologies continue to advance, more and more fields and buzzwords start to pop up around the subject. Often terms such as ‘artificial intelligence’ and ‘machine learning’ are used interchangeably. This sparks much confusion. This paper will look to differentiate these two terms and bring some clarity to what they actually mean.
The term ‘Artificial intelligence’ was coined in the 1950s. This occured when the first academic conference convened to discuss the rising possibility of ‘smart’ computers.
On a pure definition level, artificial intelligence refers to any computer system that has the ability to replicate the way humans think or operate using a multitude of tools such as logic and if-then rules. Anything from a virtual travel booking agent to a self-driving vehicle can be defined as an artificial intelligence. As long as a computer is able to make decisions that mimic the way humans make decisions, it can be categorized under the more general term of artificial intelligence.
The term machine learning popped up a couple years after artificial intelligence. And ML can be categorized as one of the tools that artificial intelligence uses. A more literal definition is that machine learning refers to whenever a computer uses data and experience to improve at tasks.
The future of machine learning : Introduction to Artificial Intelligence & Machine Learning
One great example of machine learning is a recommendation engine — services such as Google and Netflix learn your interests by collecting your data from what you search and watch, and using this information have personalized recommendations for you as an individual.
Machine learning is an integral part of most organizations and businesses today. The last decade has seen machine learning rise in popularity due to significant advancements in research as well as applications in most sectors of technology. Be it healthcare, digital marketing, finance, it has become an essential asset in all realms of establishments. Machine learning is still a relatively new technology concerning the experience industries have used it. Ml’s innovation rate is its best quality. This has been going on for the better part of the last decade. Naturally, it’s safe to say the future of machine learning is very bright.
Machine learning started with simpler models which provided close to satisfying results. Soon the models became complex and the datasets started to become increasingly large. This prompted the need for deep learning models which provided more accurate results and benefited from the large amounts of data.
Deep learning became immensely popular especially for its applications regarding image and textual datasets.
Convolutional neural networks became very popular and saw their use in most applications. Natural language processing models have had a ton of successful research and developments in the last 4-5 years creating complex and powerful models. The recent success of models such as GPT-3, shows a trend of machine learning models becoming more and more complex. The number one rule according to Google’s Machine Learning handbook states that if you can build a simple rule-based system that doesn’t require machine learning, then do that.
I believe this is where the future of machine learning lies. Simpler models that can do the job will be more beneficial compared to the complicated massive architectures that are being used today. Models today have hundreds of layers and millions of parameters which makes deep learning a black box technology. Tuning the models for specific applications is very hard to do when it is difficult to understand how each layer’s output affects the overall models’ output. Some applications help us visualize these intermediate outputs, but for huge models, it becomes increasingly complicated to understand and tune all the layers.
This is why we believe the future of machine learning lies in explainable AI. This is a concept of using a white-box approach to building the system rather than the current one. Models are built in such a fashion that each layer output is understood by the people building/using it and its effect on the overall model can easily be understood. This technology becomes extremely important and useful when using machine learning in areas such as healthcare and defense, where mistakes cannot be afforded and the AI can be trusted to provide the best and most meaningful solution. The human in the middle approach in explainable AI helps avoid the anomaly for a given use-case as the human understands the AI and can explain its result for any situation.
Another area which we believe will define the future of machine learning is the approach. It is no surprise that the majority of the research in the last decade has been model-centric. The focus has always been on improving the model and its performance. But for machine learning, the data is far more important. To achieve better results from applications, our focus must start being more data-centric. A model is first defined by the volume, quality, and correctness of the data.
Best Form of Data
Having incorrect data, noisy data, useless data, or not enough data affects the model in significant ways. That even with the best algorithms the results will end up poor.
The increasingly popular AutoML or automated machine learning is also a technology that will define the future of machine learning. AutoML helps automate the processes in the pipeline of a machine learning application. Data preprocessing, feature extraction, feature engineering, hyperparameter optimization, and deployment can all be performed with AutoML with the advantage of not doing any of these tasks manually. As a data scientist, this is beneficial to save time for smaller tasks and also to check for additional solutions to the problem which he may have missed. Another huge advantage is extending the technologies of machine learning and AI to a wider audience that may not have the most knowledge of machine learning. This enables them to perform their task without worrying about the model or its intricacies. This makes machine learning more accessible and more valuable to a larger audience.
These are only a few scopes of the vast boundary of the future of machine learning. And this is why the future of machine learning is bright and will continue to do so for a long time.
Introduction to Artificial Intelligence & Machine Learning : In Conclusion
Machine learning is the study of making computer algorithms that improve automation learned from data. This helps in making a computer “sentient”. Machine learning’s reach can be categorized into two main groups:
1) Supervised learning: the machine learning technique of training a function that maps an input to an output based on the data provided.
2) Unsupervised learning: a machine learning technique in which the data scientists need not train the model. However, the training model works on its own. The model wants to identify patterns that were previously undetected. It mainly deals with the unlabelled data.
Machine learning has become increasingly popular and has reached human-level performance in many challenging areas such as image recognition and finance.
This development has been fuelled by the following:
1) An unprecedented increase in data volume makes it challenging for data scientists to extract the relevant information using conventional methods.
2) An increase in knowledge of AI and machine learning, contributing to the development of machine learning applications that are made to cater to the needs of specific applications.
3) Freely available, open-source software frameworks that are easy to use and allow the development of complex machine learning applications based on a couple of hundred lines of Python code.
Google open-sourced their hugely popular machine learning project “TensorFlow.” Which already was an active project being used in various fields. If the current trend is of any indication, algorithms and machine learning are going to dominate the tech world for a very long time. The supply-demand gap for machine learning has been going up and the wars among the tech giants have also been getting fierce.
The future of machine learning looks really exciting.
Currently, pretty much every industry uses machine learning. The idea of working on a new domain in the present day without the application of machine learning seems rather daunting. During the post-industrialization time, individuals have attempted to make machines that act and do every activity just like a human. As a result, machine learning has become AI’s greatest blessing to the human race for the effective realization of the targets. The rise of machine learning has also changed the way firms hire employees.
- Search results optimization:
SEO is developing at a rapid pace with the application of machine learning. It helps us far better understand the intent – a very important signal for content that is relevant and of such high quality. It helps us filter through massive amounts of data, get insights, and perform tedious tasks. The SEO community must adapt and help search engines connect users with the right content at the right time and deliver the best content experience.
People have started using ML in finance more these days due to the vast availability of data. It is reshaping the financial services industry like never before. Big banks and companies in the financial services industry are deploying ML. With the idea to streamline their processes, optimize portfolios, decrease risk, and underwrite loans amongst other things.
ML can potentially improve the treatment protocols and outcomes through algorithmic processes. For, example, Deep learning is used in both radiology and medical imaging. With the use of neural networks, deep learning can detect, recognize, and analyze cancerous lesions from images. With the help of machine learning, the processing speeds have increased. And cloud infrastructures are able to detect anomalies in images beyond what the human eye can see. This is helping in diagnosing and treating diseases.