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Tag: Artificial Intelligence

Quit Playing Games With Artificial Intelligence – Its Serious Business Now!

Posted on by Imarticus Learning
Reading Time: 3 minutes

The gaming industry is no longer a simple and cheap way to keep restless children occupied during their summer vacations. People of all ages actually spend hours together in front of their gaming consoles playing a variety of games. The quality of games has improved beyond recognition today, keeping serious gamers glued to their games for long periods of time, helping make these games economically feasible.

One big change in recent years that has turned the gaming industry around is the development of artificial intelligence and virtual reality. Let us see a few ways in which this has happened.

Gaming Realism

We have seen how virtual reality helps to generate 3D images or an overall environment that interacts with reality. We have all been amazed and impressed to see a real character in a VR environment wave his or her hands in the air and conjure up a screen on which different dials and buttons are available. Similar virtual reality environments inside games have added a touch of the realistic to these games.

Adaptive Environment

Unlike the static code of earlier games, where a certain action X by a character or the player would result in a fixed outcome Y only. But with the introduction of artificial intelligence, the environment and the responses to actions could be varied, with the game throwing up different responses in different scenarios.

The Move to Responsive

Most of the activities we do today are moving from the computer to our mobile phones, like watching sports or news or looking at weather forecasts, ordering takeaway, booking tickets, etc. The situation is no different for the gaming industries. They are having to adapt to this scenario and create games that are easy to view and easy to maneuver on mobile phones. Responsiveness is the new buzzword.

Heavy Computing Power

This is a phenomenon observed in all our gadgets like computers, mobile phones etc. There has been a surge in computing power. This has affected gaming as well, with super-fast responses from the characters. This is because the gaming consoles now carry unbelievable computing power.

Machine Learning

Artificial intelligence in gaming consoles is encouraging the gaming programs to learn from past experience and adjust its responses accordingly, making the gaming experience more difficult for the players. The games are getting smarter because of the use of these artificial intelligence tools, making them all the more challenging for the players.

Real-Time Reactions

Games earlier were one-dimensional collections of graphics and code which threw up situations for the gamers, to which they would provide certain reactions, to which the game would again provide a certain response. This was done with the help of a detailed algorithm which dictated the machine’s response. But now, with AI, the events happening within the game would influence the reaction of the computer, and these changes in reaction would also go into its knowledge bank and contribute to its machine learning.

Developer Skills

One more aspect of the industry changes in the gaming industry is that the developers writing the code for these games now have to contend with all the changes listed above, and therefore have to pick up adequate skills for incorporating elements of artificial intelligence and virtual reality in these games.

Industry Change

The gaming industry is seeing far-reaching changes as a result of the addition of virtual reality elements into games. The gaming experience becomes much more rich and intense for the player, therefore making them more willing to fork out much higher prices for the games they buy. Advertisements linked to different games have also become more visible, providing gaming companies to look at a rich stream of revenue.

Should You Fear Machine Learning?

Posted on by Imarticus Learning
Reading Time: < 1 minute

Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The goal of machine learning is to get computers to learn in a similar manner to humans.

Machine learning is a type of artificial intelligence that helps computers learn without having to be programmed by a person. These computers are programmed in a way that focuses on data that they receive on a regular basis. This data can then help the machine “learn” what preferences are and adjust itself accordingly.

Nowadays, the development in Artificial Intelligence (AI) has brought us to the stage where organizations are using various algorithms, analysis, and experience to learn and program themselves without human intervention.

This type of procedure will create changes in too many industries. The use of machine learning has grown exponentially in the past few years, and you may not realize how widely it is used.
Following stats is just tip of the iceberg:

  • 85% of customer interactions will be managed without humans by 2020.
  • 38% of jobs could be replaced by AI/machine learning by the 2030s.
  • 20% of top executives rely on machine learning to run their businesses.
  • 48% projected growth in the Automotive Industry by 2025.

Source: jigtechnologies.com; elearninginfographics.com; pwc.co.uk; mckinsey.com

Finance Activities That Can Be Easily Automated in Artificial Intelligence

Posted on by Imarticus Learning
Reading Time: 3 minutes

A lot of companies have traveled the distance from in-house talent to outsourcing for a number of functions. One of the functions that has seen these changes is finance. Instead of using regular employees, many companies have outsourced this function to dependable and efficient outsourcing companies who take care of the security of data and also carry out all the requirements efficiently. But many companies are now ready to take the next step forward by automating their finance function instead of outsourcing it, and then getting the benefits of artificial intelligence to do those tasks even better. Not all functions inside finance are easily automobile, though. Automation is a possibility only for recurring and monotonous activities which do not need too much of strategic thinking or knowledge of the company’s confidential data.
Also Read: Artificial Intelligence in Investment Banking – The Technological Revolution

These are the finance functions which lend themselves easily to automation :

Bookkeeping and Accounting 

For a human resource, this can be a very monotonous job to keep track of assets, liabilities, receivables and payables on a daily basis. Worse, this is prone to human error if it is done by company’s own resources or by outsourced agencies. Using the accounting principles accepted in the country of operation, the accounting activities of regular bookkeeping and doing reconciliations can easily be done by an automated program that draws inputs from the company’s connected systems, and produces outputs on daily, weekly and monthly basis.

Payables and Receivables :

If a company can centralize and automate the work of generating invoices and receipts for payments due and payments received, then the accounts of such amounts can also be easily automated. The tool that is used to do this can also be set up to validate and self regulate the system so that double invoicing can be avoided, and all receipts are accounted for. The respective managers in the department could also generate daily or weekly reports to keep track, or they could set up the tool to automatically generate a notification or a report when any document is created for payables or receivables.

Salaries and Commissions

Salaries are usually the easiest to automate, because the database of employees and the preconditions for specific heads of the salary can be programmed once and don’t need to be tinkered with again till a person’s salary undergoes a change. The more difficult part is to incorporate the variable part of the salary into the final output, because such variable payouts could be dependent on a number of dynamic factors, and in some cases, might also be discretionary. But whether fixed or variable, an automated tool can easily take care of the calculations, generate salary and commission statements, and also send a payment advise directly to the bank, so that the money can be transferred to the employee or partner’s account or a cheque can be created.

Transaction Audits 

There are some repetitive financial transactions that need to be audited by internal or external auditors every quarter or at least once a year. Such transactions offer a good potential of being automated. The reason is that they are usually in large numbers and can be automatically vetted on a daily basis or even real time as they take place, instead of waiting till the end of the month or quarter. And the risk of human error can be completely done away with by automating those tasks. The audit of the judgemental parts of finance functions, which involve correct interpretation of company rules or federal laws, can be left to the traditional auditors.
A number of functions are going in for the benefits of automation, and there is no reason for finance to get left behind. The only need is to correctly identify which parts can be automated with no risk attached.
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7 Key Skills Required For Machine Learning Jobs!

Posted on by Imarticus Learning
Reading Time: 3 minutes

Overall, 2017 saw an upward trend in talent acquisition across Machine Learning. This will further increase in 2018.
With technology such as Machine learning, AI, and predictive analytics reshaping the business landscape, software products, aggregators, Fintech, and E-commerce will drive the demand for technology professionals in India.

Machine Learning is usually associated with Artificial Intelligence (AI) that provides computers with the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programmed. It focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data.

Now, are you trying to understand some of the skills necessary to get a Machine Learning job? A great candidate should have a deep understanding of a broad set of algorithms and applied math, problem-solving and analytical skills, probability and statistics, and programming languages.

Here is a list of key skill sets in detail:

Programming Languages like Python/C++/R/Java

If you want a job in Machine Learning, you will probably have to learn all these languages at some point. C++ can help in speeding code up. R works great in statistics and plots, and Hadoop is Java-based, so you probably need to implement mappers and reducers in Java.

Probability and Statistics

Theories help in learning about algorithms. Great samples are Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models. You need to have a firm understanding of Probability and Stats to understand these models. Use statistics as a model evaluation metric: confusion matrices, receiver-operator curves, p-values, etc.

Data Modeling & Evaluation

A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure (e.g. log-loss for classification, sum-of-squared-errors for regression, etc.) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.)

Machine Learning Algorithms

Having a firm understanding of algorithm theory and knowing how the algorithm works, you can also discriminate models such as SVMs. You will need to understand subjects such as gradient descent, convex optimization, quadratic programming, partial differential equations, and alike.

Distributed Computing

Most of the time, machine learning jobs entail working with large data sets these days. You cannot process this data using a single machine, you need to distribute it across an entire cluster. Projects such as Apache Hadoop and cloud services like Amazon’s EC2 makes it easier and cost-effective.

Advanced Signal Processing Techniques

Feature extraction is one of the most important parts of machine-learning. Different types of problems need various solutions, you may be able to utilize really cool advanced signal processing algorithms such as wavelets, shearlets, curvelets, contourlets, bandlets.

Other skills:

  1. Update yourself:

    You must stay up to date with any up and coming changes. It also means being aware of the news regarding the development of the tools (changelog, conferences, etc.), theory, and algorithms (research papers, blogs, conference videos, etc.).

  2. Read a lot:

    Read papers like Google Map-Reduce, Google File System, Google Big Table, The Unreasonable Effectiveness of Data.

The next question you would have is, “What can I do to develop these skills?” Unless you already have a strong quantitative background, the road to becoming a Machine Learning Specialist will be a bit challenging – but not impossible.

However, if it’s something you’re sincerely interested in and have a passion for Machine Learning and lifelong learning, don’t let your background discourage you from pursuing Machine Learning as a career.

Related Post:  What is The Easiest Way To Learn Machine Learning?

Top 5 Data Science Trends in 2018!

Posted on by Imarticus Learning
Reading Time: 2 minutes

Data Science in today’s world is a combination of various functions – AI, Deep Learning (real and hyped progress), Quantum Computing, Big Data, IoT, and many more such applications which are used together as a network. 2017 was dominated by advances in the AI space which had taken over from Big Data. Data has become popular due to the open-source regime which is slowly chipping away at the market and technology shares of established names like Oracle and  Microsoft. With the ever-increasing popularity of newer and scalable programs, let us see the top trends to expect in 2018.

Also Read: How to Become A Data Scientist?

Regulation

The awaited impact-event will be GDPR (European General Data Protection Regulation) which will become enforceable on May 25, 2018. This regulation will affect data science practice in three areas – limits to be applied on data processing and consumer profiling, “automated decision making” and the right to an explanation for that, and feeding in biases and discrimination in automated decisions.

The measures under this act were approved by the European Parliament on April 27, 2016, and will go into effect on May 25, 2018. The law will focus on the new rules on the collection and management of personally identifiable information (PII) of EU citizens. Implementing these rules will bring broad changes in the big data modeling and in creating predictive models.

Artificial Intelligence

According to Garter’s list of Top 10 tech trends in Big Data, it is laying the foundation of AI across organizations. It will remain a major challenge and work plan to follow through till at least 2020 as significant investment in skills, processes and tools will be required to exploit these techniques.

Intelligent Apps

These will be created and used with an aim to enhance human activity and effort and mostly not replace it. Augmented analytics is a strategic growth area in which machine learning will be widely used to automate data preparation, insight discovery, and sharing for a large range of business users, operational workers, and citizen data scientists.

Virtual Representations of Real-World Objects or Systems

Digital representations of the real-life objects will be a common reality and their inter-linkages will help in checking the cause and effect changes for improving the operations and value. It is predicted that over time digital twins of every physical reality will be available and infused with AI capabilities to enable simulation, operation, and analysis. This will particularly help in fields of city planning, digital marketing, healthcare, and industrial planning.

Cloud to the Edge

Edge computing works to maintain the closeness of processing, content collection and delivery close to the source of information. This helps in reducing issues to latency, bandwidth, connectivity. Garter predicts that pairing this strategy with cloud computing will give the best of both the worlds to create a service-oriented model and a centralized model and coordination structure.

While many trends will take a long while to cultivate from its conceptual stage to a working philosophy, these trends will lead the way for future innovations.
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Master the Skills of Fintech And Be Successful

Posted on by Imarticus Learning
Reading Time: 2 minutes

Master the Skills of Fintech And Be Successful

FinTech is a financial technology that covers a large group of organisations utilising programming and innovation to give monetary administrations. In FinTech, the majority of candidates will have a software engineering qualification.

Even though the industry is a combination of finance and technology, so when looking for the skills that have to be appropriate for being a part of this FinTech industry, technology comes first.
As the financial industry is progressively encountering changes, experts are on the hunt for skilled and trained people. The following are the overview of skills and capabilities emphatically looked for to work in FinTech.
Communication skills
Regardless of whether you work with merchants, business analysts or IT people, you must have the capacity to clarify parts of your tech venture unmistakably and compactly to your client.
It is imperative to understand that fintech courses aren’t only about developments and advancements, fintech includes more things. New companies need to sell their products and services, and, consequently, they require great managers and client relations experts, who have great communication abilities, having the capacity to clarify the key parts of the product. So you need to master this skill to be successful.
Teamwork abilities
In FinTech, you’ll be working with various individuals at various phases of a venture. You’ll frequently work under strain and to tight due dates to get the work completed, which implies you’ll need a decent association with the colleagues to request to ensure the work gets conveyed on time. So be prepared to work hard on your skills to achieve your objectives.
Problem-solving capacity
Working in FinTech is not an easy job, you’ll continually be searching for approaches to make things work quicker and all the more proficiently. You need to manage data and information and lessen the risk factor constantly. The key thing is having the capacity to know and understand an issue, divide it into its basic components and after that work out how technology can help you out. This is maybe the ability you have to work on
Gain work experience
You can gain work experience through an internship. A practical experience that one can gain over the education will likewise have an upper hand over the individuals who have classroom experience.
So working as an intern will give you much more experience and will boost up your skills.
AI and ML Knowledge
AI and ML stand for artificial intelligence and machine learning respectively. They both are becoming progressively essential in the field of FinTech. Machine Learning and Artificial intelligence specialists are highly looked after by investment banks to enable them to actualize financially effective solutions and show signs of improvement in client experiences.
Cyber security expert
Over the most recent couple of years, there is a huge increment in cyber crimes with finance being the main purpose behind these crimes. For somebody working in FinTech, knowledge about cyber security is a basic piece of the job. In case you’re working as a software designer, understanding and knowledge of cyber security is expected to create secure applications and programming software, so that client data and information are secured.
Knowledge about finance
Meanwhile, focus on building up your insight into the financial sector. Stay up to date with the latest updates in the markets of the finance world. You can upgrade your knowledge by perusing the Financial Times and the Economist, or viewing Bloomberg TV, and also there are so many blogs to read.

5 Top Reasons to Learn Python

Posted on by Imarticus Learning
Reading Time: 2 minutes

One should have a good grasp of technology, as its uses and advantages have seeped in almost all spheres of professional setups. If you are working in the field of IT, programmer to be specific, a quick way to upgrade your resume would be to learn Python. Python is considered to be the most commonly used programming languages. Hence for a programmer who is on the brink of embarking his career should learn Python.
So if you are considering learning to code, and be updated and efficient with your skills in the world of programming. Then further read on to understand five undisputable reasons you should learn Python.

Quick and Fast

Python is definitely an easy language to learn, to be true the language was designed keeping this feature in mind. For a beginner, the biggest advantage is that the codes are approximately 3-5 times shorter in Python than in any other programming language. Python is also very easy to read, almost like reading the English language, hence it becomes effective yet uncomplicated in its application.
The dual advantage is that a beginner will not only pick up faster but, will also be able to code complex programmes in a shorter amount of time. And an experienced programmer will increase productivity.

Big Corporates use Python

Python is one of the most favourite languages used at Google, and they are ever hiring experts. Yahoo, IBM, Nokia, Disney, NASA all rely on Python. They are always in search of Python web developers, and a point to note is that they are big pay masters. Hence learning Python equals to big Pay cheques.

Python for Machine Learning and Artificial Intelligence

The biggest USP of Python is that it is easy to use, flexible and fast, hence it is the preferred language choice. And especially so in computer science research. Through Python, one can perform complex calculation with a simple ‘import’ statement, followed by a function call, thanks to Python’s numerical computation engines. With time Python has become the most liked language for Machine Learning.

Python is Open Source and comes with an exciting Ecosystem

Python has been there for almost 20 years or so, running across platforms as an open source. With Python, you will get codes for, Linux, windows and MacOS. There is also a number of resources that get developed for Python that keeps getting updated. It also has a standard library with in-built functionality.

Nothing is Impossible with Python

And if the above reasons are not convincing, perhaps the best reason to learn Python, is that irrespective of what your career goals are you can do anything. Since it is easy and quick to learn, with it, you can adapt to any other language or more importantly environment. Be it web development, big data, mathematical computing, finance, trading, game development or even cyber security, you can use Python to get involved.
Python is not some kind of a niche language, and neither is it a small time scripting language, but major applications like YouTube or Dropbox are written in Python. The opportunities are great, so learn the language and get started.

References:

Python Coding Tips For Beginners

Top Resources To Learn Python Online In 2022

Top Resources To Learn Python

It is Useful To Learn Python Language For Big Data

AI (Artificial Intelligence) is about to Reshape the Workplace. How?

Posted on by Imarticus Learning
Reading Time: 3 minutes

Of late, a great topic of discussion is on the true meaning of Artificial Intelligence. As the field is witnessing progress there is a constant evolution in the meaning as well. In simple words, artificial intelligence is the ability of the computer to perform tasks commonly associated with intellectual human beings. It is predicted to fundamentally reshape the way in which organizations work.

Artificial intelligence and Machine intelligence is often misunderstood as a substitute for each other. Machine learning is basically getting the computers to program for themselves, here it allows the data to internally train the data sets. Machine or Artificial intelligence on the other hand means ‘intelligent computers’, computers without human intervention capable of pattern discovery, discerning context, to reason, and to learn and improve themselves over time.

What’s in the future……?
In the future with this type of evolution, it will not necessarily be Humans v/s Computers, but man and computers working alongside in harmony to improve the way we work. So the employees who do routine manual or routine cognitive job roles will have a high chance that their jobs will be replaced with computers and they will have the availability of time to invest in areas that they are interested in or jobs that require advanced skills.

Making machines responsible to do the repetitive tasks can go a long way. It will take the creativity and innovation quotient of the human race to enhanced levels. Furthermore, this technology is not only set to impact the workers with routine repetitive jobs, if your job is of routine cognitive nature then artificial or machine intelligence will play a role of a digital advisor, but it will also play a role where man and machine collaboratively work in tandem for betterment. Artificial learning and automation of certain jobs will then to a large extent become a good thing.

There are some researches that predict that artificial intelligence will be responsible for making workers more productive and create new jobs. There is a belief that AI will help with automation that will not only assist companies to focus on higher-skilled tasks and more creative jobs but will give them insights that will enable workplaces to use that knowledge in ways that cannot have been imagined so far.

Some stats on Artificial Intelligence:

  • By 2020 85% of customer interactions will be handled without a human
  • 44% of executives believe “artificial intelligence’s most important benefit is that automated communications that provide data that can be used to make decisions”
  • By 2018 ‘customer digital assistance’ will recognize customers by face and voice across channels and partners.
  • By 2020, smart agents will manage 40% of mobile interactions.
  • 9% of business data technology has artificial intelligence solutions deployed.

The artificial intelligence market is estimated to reach $40 billion by 2020
In the future, you will witness an Artificial intelligence revolution in marketing, where smart data and machine intelligence in collaboration will use artificial intuition which ape’s human intuition. For e.g. creating advertisements whose images and phrases evolve based on viewer response.

However, reaching the state where organizations will be machine intelligence is not so easy. The majority of our efforts at present is on supervised learning, where we are training the computer on instances that are labeled with reinforcement, and doing that takes time. Also, there is the impending challenge of embedding technology into existing enterprise applications.

In doing that we will one day make the computer as intelligent as the human brain. 25 years ago the internet impacted the wider world, it revolutionized the way we existed, and changed the way organizations functioned. We are at the identical brink of time wherein the next 25 years Artificial Intelligence might have the same impact on the way we work and live.

Read More: Imarticus Learning

The Promise of AI: The Value of Artificial Intelligence and its Applications

Posted on by Imarticus Learning
Reading Time: 2 minutes

Read the previous part here.
When it comes to predicting the value of AI, things can get a little vague and confusing. This is mainly because of the wide applications as well as the rapid evolvement of Artificial Intelligence. It is estimated by the Data Corporation, that the market for technologies of or related to Artificial Intelligence, is bound to increase up to $40 billion by the year 2020. While this market would deal with all those technologies that help in analysing unstructured data, it is believed that it will be generating productive improvements, which promise to be well over $60 billion worth of the United States market per year.
Consequently, a majority of investors have begun to discover and realize the true value of AI and as a result of this, more and more investments have begun to take place in the venture capitalist markets. Let’s talk about figures, the year 2013 saw about $757 million, being invested in AI start-ups, 2014 saw about $2.8 billion, 2015 saw around $2.3 billion and if these figures are anything but telling, it is guaranteed that the investments will only be growing in proportion for the coming years. The Mckinsey Global Institute has estimated that all the automating knowledge work, with Artificial Intelligence, is bound to generate anything around $5.2 trillion to close to $7 trillion. It is believed that the advancement in advanced robotics by primarily relying on Artificial Intelligence will generate anything up to $2 trillion. All of these estimates are made for up to the year 2025, which is barely a decade away.
Thus, we can conclude that the value of Artificial Intelligence is not only humongous, but it bound to increase manifold soon enough. It is believed by many experts that the AI has so much power, that it can go ahead to solve global issues like climate change and food insecurity. Artificial Intelligence already has and is bound to have a varied number of applications. Mapping poverty with satellite data, measuring literacy rates, cracking down on human trafficking rackets, preventing abusive internet tools from taking up actions are some of the social applications of AI that will definitely benefit the society in the near future.
When it comes to public safety, artificial intelligence could very well be applied for pinpointing the various happenings during crimes, for prediction of crime hotspots, for disposing of car bombs autonomously, for predicting the fire or earthquake risks of building accurately, for ensuring that every type of security screening is less invasive and so on. Industries would benefit majorly from AI as it would ensure the prevention of breakdowns or power cuts before they even take place, everything from intelligent manufacturing to automated factories could be very possible, improvement in terms of the dairy supply chains with the help of market forecasting would be a real thing and much more.
Thus AI shows a lot of promise in terms of changing the world for the better tomorrow and as a result of this, it would make a great career. There are many institutes in India like Imarticus Learning, which offer a number of comprehensive courses in Data Science, that would help an individual further their career in AI.

Loved this report? Read similar blogs here:
Investment Banking Soon to be Taken Over by Artificial Intelligence
Can the Big Data Dilemma be solved by AI?
Technology Buzzwords: Buzzword Watch – 2017

The Promises of Artificial Intelligence: Introduction

Posted on by Imarticus Learning
Reading Time: 2 minutes

The field of Artificial Intelligence seems to working on a winning streak. In the year 2005, the U. S Defence Advance Research Project Agency, held the DARPA Grand Challenge, which was supposedly held to spur development of autonomous vehicles, basically in order to make self-driven, smart cars. This challenge was taken up and successfully completed by 5 teams. In the year 2011, in a great competition of Jeopardy, the IBM Watson system, was successfully able to beat two long time, human champions of the same legendary game. Another great win of technology over the human race would be in the year 2016, when Google DeepMind’s AlphaGo system was able to successfully defeat the world champion of Go Player, who was reportedly the world champion for 18 consecutive times.
While these feats of technology over the human brain are extremely commendable, today the long surviving dream of humans, which basically revolved around developing technology to control their surroundings, has finally come to fruition. This has resulted in the form of Google’s Google Assistant, Microsoft’s Cortana, Apple’s Siri and Amazon’s Alexa. As a result of all of these AI (Artificial Intelligence) powered virtual assistants, people are able to make greater use of technology in order to live better lives.
Artificial Intelligence is considered to be a field of computer science, which is entirely devoted to the creation of computing machines and systems, all of which are able to perform operations that are similar to human learning and decision making. According to the Association for the Advancement of Artificial Intelligence, AI is, “the scientific understanding of the mechanisms underlying thought and intelligent behaviour and their embodiment in machines.” While these intelligence levels can never be compared to those of the humans, but they can certainly vary in terms of various technologies.
Artificial Intelligence includes a number of functions, which include learning, which primarily includes a number of approaches such as deep learning, transfer learning, human learning and especially decision making. All of these functionalities can later help in the execution of various fields such as cardiology, accounting, law, deductive reasoning, quantitative reasoning, and mainly interactions with people, in order to not only perform tasks, but also to learn from the environment.
While the recent changes may be extremely mind blowing, the promise of AI has always been existing since era of electromechanical computing, this began in the time period, after the World War 2. The first conference of Artificial Intelligence was held at the college of Dartmouth in the year 1956 and at that time, it was said that AI could be achieved within the time period of summer. Later on, in the 1960’s there were scientists, who claimed that in the next decade, it would be possible to see various machines controlling human lives. But it was in the year 1965, when the Nobel Laureate, Herbert Simon, who is supposed to have predicted the words, which would have some substance and which were, “In the next 20 years, it would be possible that machines would be able to do any work of labour that man can”.
With Artificial Intelligence, going in full fervour, the field which it has affected most in the field of Data Science. And as there are many who believe that there is a great to achieve in this field, have begun to get trained in the same by approaching professional training institute – Imarticus Learning.