Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.


Machine learning algorithms are often categorized as supervised or unsupervised.

  • Supervised machine learning algorithms can apply what has been learned in the past to new data using labeled examples to predict future events. Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system can provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors to modify the model accordingly.
  • In contrast, unsupervised machine learning algorithms are used when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.
  • Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning since they use both labeled and unlabeled data for training — typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method can considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled and relevant resources to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.
  • Reinforcement machine learning algorithms are a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behavior within a specific context to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.


Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal.

Applications of Artificial Intelligence

The applications for artificial intelligence are endless. The technology can be applied to many different sectors and industries. AI is being tested and used in the healthcare industry for dosing drugs and different treatment in patients, and for surgical procedures in the operating room.

Other examples of machines with artificial intelligence include computers that play chess and self-driving cars. Each of these machines must weigh the consequences of any action they take, as each action will impact the end result. In chess, the end result is winning the game. For self-driving cars, the computer system must account for all external data and compute it to act in a way that prevents a collision.

Artificial intelligence also has applications in the financial industry, where it is used to detect and flag activity in banking and finance such as unusual debit card usage and large account deposits — all of which help a bank’s fraud department. Applications for AI are also being used to help streamline and make trading easier. This is done by making supply, demand, and pricing of securities easier to estimate.

Categorization of Artificial Intelligence

Artificial intelligence can be divided into two different categories: weak and strong. Weak artificial intelligence embodies a system designed to carry out one particular job. Weak AI systems include video games such as the chess example from above and personal assistants such as Amazon’s Alexa and Apple’s Siri. You ask the assistant a question, it answers it for you.

Strong artificial intelligence systems are systems that carry on the tasks considered to be human-like. These tend to be more complex and complicated systems. They are programmed to handle situations in which they may be required to problem solve without having a person intervene. These kinds of systems can be found in applications like self-driving cars or in hospital operating rooms.

CASE STUDY: How Tesla is making use of Artificial Intelligence in its operations

ARTIFICIAL INTELLIGENCE IN CAR MANUFACTURINGCar manufacturers all over the globe are using artificial intelligence in just about every facet of the car making process. AI can be witnesses working its magic through robots putting together the initial nuts and bolts of a vehicle or in an autonomous car using machine learning and vision to safely make its way through traffic.

AI Driving Features

When it comes to driving, cars with artificial intelligence offer two levels of functionality: driver assist and fully autonomous mode.

Here are the differences:

Driver Assist

Before the automotive industry is comfortable letting AI take the wheel, it first wants to put it in the co-pilot’s seat.

AI lends itself perfectly to powering advanced safety features for connected vehicles. And that helps customers, manufacturers, and regulators get comfortable with AI as the driver before it gets its own license to drive.

By monitoring dozens of sensors, AI can identify dangerous situations. It can then alert the driver, or take emergency control of the vehicle in order to avoid an accident. Emergency braking, cross-traffic detectors, blind-spot monitoring, and driver-assist steering can help avoid accidents and save lives in the process.

Driverless Automobiles

The mechanical muscle needed to control vehicle steering, braking, and acceleration has been within reach for nearly a century. The reason autonomous cars are not jamming the streets now is that, until recently, they didn’t have a brain.

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Tesla and Artificial Intelligence

“We develop and deploy autonomy at scale. We believe that an approach based on advanced AI for vision and planning, supported by efficient use of inference hardware is the only way to achieve a general solution to full self-driving.” — stated by Tesla on its official site

Unlike its competitors, Tesla’s biggest USP isn’t just producing automobiles but also technologies. Tesla specializes in making use of high tech technologies to deliver luxurious long-range electric automobiles.

“The advantage that Tesla will have is that we’ll have millions of cars in the field with full autonomy capability and no one else will have that,” — Elon Musk

Throughout its journey, AI and Big Data have remained steady partners of the firm. Tesla has taken excellent use of AI and Big Data for expanding its customer base. The firm has made use of existing customer databases for its data analytics using it to comprehend customer requirements and regularly updating their systems accordingly.

In the case of Artificial Intelligence, Tesla has leveraged it to focus on mainly 2 areas: All electric propulsion and autonomous driving.

Recent AI Tools leveraged by Tesla

Initially, Tesla had collaborated with Nvidia to optimize it’s AI integrated chips. Later dropping Nvidia, the company vowed to create its own chips. With these chips, the firm aims to ensure that the cars are able to navigate through not only the freeways but also through local streets as well as traffic signals.

In a recent Hot Chips conference Tesla confirmed that its performance has largely boosted owing to the heavy optimizations in the AI chip. A massive number of transistors have been used — 6 billion — which constitute the processing circuitry for each of Tesla’s chips.

Tesla’s in-house expertise in the case of software and battery manufacturing has also helped in giving it an edge over its fellow manufacturers. The firm’s new AI technology aims at setting a milestone for mass-market automation for cars.

Dual Chips

The Tesla system consists of two AI chips in order to support it for better road performance. Each of the AI chips makes a separate assessment of the traffic situation for guiding the car accordingly. The assessment of both chips is then matched by the system and followed if the input from both is the same.

In case of any discrepancy, a revaluation is done until a safe decision is taken. With the purpose of properly safeguarding the car against failure, it has surplus power as well as data input feeds so that the car can resume working in case of a single unit failure though it’s spare units. Through these abundant features the firm is ensuring that in case of an unanticipated failure, the car will be well equipped to avoid any accidents.

Optimized Design

These AI chips have been optimized to run at 2 GHz and perform 36 trillion operations per second, achieving this level of performance by dismissing all generic functions and channeling the focus on only the important ones. Having taken over 14 months of severe research and involvement the chip was designed with Samsung now manufacturing the processor. The chip will be installed in both the new Tesla cars as well as the old models.

Acknowledged as one of the most aggressive developers in the market, Tesla has always been a firm that has made data collection and analysis of the biggest wielded weapon for everything it does. When it came to developing their own chips, they made no exception. Artificial intelligence and data analysis has enabled the company to design autonomous cars with the potential to revolutionize the way we drive cars.

Machine Learning Algorithms in Autonomous Driving

Autonomous cars are very closely associated with Industrial IoT. IoT combined with other technologies such as machine learning, artificial intelligence, local computing, etc are providing the essential technologies for autonomous cars. Very inquisitive questions for many is how are these autonomous cars functioning. What actually is working inside to make them work without drivers taking control of the wheel. Very well known that these days cars are equipped with a lot of sensors, actuators, and controllers. These end devices are driven by software sitting on various function-specific software running on ECUs ( Electronic Control Units). Machine learning software is also part of this set.

One of the main tasks of any machine learning algorithm in the self-driving car is a continuous rendering of the surrounding environment and the prediction of possible changes to those surroundings. These tasks are mainly divided into four sub-tasks:

  • Object detection
  • Object Identification or recognition Object classification
  • Object localization and prediction of movement

Machine learning algorithms can be loosely divided into four categories: regression algorithms, pattern recognition, cluster algorithms, and decision matrix algorithms.

Regression Algorithms

In ADAS, images (radar or camera) play a very important role in localization and actuation, while the biggest challenge for any algorithm is to develop an image-based model for prediction and feature selection. The type of regression algorithms that can be used for self-driving cars is Bayesian regression, neural network regression, and decision forest regression, among others.

Pattern Recognition Algorithms (Classification)

In ADAS, the images obtained through sensors possess all types of environmental data; filtering of the images is required to recognize instances of an object category by ruling out the irrelevant data points. Pattern recognition algorithms are good at ruling out unusual data points. Recognition of patterns in a data set is an important step before classifying the objects. These types of algorithms can also be defined as data reduction algorithms.

These algorithms help in reducing the data set by detecting object edges and fitting line segments (polylines) and circular arcs to the edges. Line segments are aligned to edges up to a corner, then a new line segment is started. Circular arcs are fit to sequences of line segments that approximate an arc. The image features (line segments and circular arcs) are combined in various ways to form the features that are used for recognizing an object.

The support vector machines (SVM) with histograms of oriented gradients (HOG) and principal component analysis (PCA) are the most common recognition algorithms used in ADAS. The Bayes decision rule and K nearest neighbor (KNN) are also used.


Sometimes the images obtained by the system are not clear and it is difficult to detect and locate objects. It is also possible that the classification algorithms may miss the object and fail to classify and report it to the system. The reason could be low-resolution images, very few data points, or discontinuous data. This type of algorithm is good at discovering structure from data points. Like regression, it describes the class of problem and the class of methods. Clustering methods are typically organized by modeling approaches such as centroid-­based and hierarchical. All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality. The most commonly used type of algorithm is K-means, Multi-class Neural Network.

Decision Matrix Algorithms

This type of algorithm is good at systematically identifying, analyzing, and rating the performance of relationships between sets of values and information. These algorithms are mainly used for decision making. Whether a car needs to take a left turn or it needs to brake depends on the level of confidence the algorithms have on the classification, recognition, and prediction of the next movement of objects. These algorithms are models composed of multiple decision models independently trained and whose predictions are combined in some way to make the overall prediction while reducing the possibility of errors in decision making. The most commonly used algorithms are gradient boosting (GDM) and AdaBoosting.

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