Artificial intelligence is reaching new heights in the medical field

Artificial intelligence is reaching new heights in the medical field

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What should I learn, AI or blockchain?

Artificial Intelligence is the science and engineering of making computer machines able to perform tasks which normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

The ultimate effort is to make a computer programs that can solve problems and achieve goals in the world, as well as humans.

There is a scope in developing the machines in game playing, speech recognition machine, language detection machine, computer vision, expert systems, robotics and many more.

70% of the companies in the world believe that they need to adopt Artificial Intelligence, in some way or another, to remain competitive by 2020. While most of them have already started using Artificial Intelligence to automate most of their work-force. The use of Artificial Intelligence has helped companies in being more productive in less time.

The biggest challenge corporates across the globe are facing is the lack of Artificial Intelligence professionals. The rate at which the field of Artificial Intelligence is growing the demand for Artificial Intelligence professionals are growing at the same rate or even faster.

Blockchain has given rise to a new field of development called blockchain development. Blockchain development involves developing and optimizing blockchain protocols and crafting the architecture of blockchain systems. A blockchain developer is responsible for creating integrated smart contracts and web applications using blockchain technology. The two types of blockchain developers are:

·        Core Blockchain Developers: They develop and design the architecture and protocols of a blockchain system and design consensus protocols and high-level development decisions related to blockchain.

·        Blockchain Software Developers: They use the protocols and architecture designed by core blockchain developers to build decentralized applications.

As Blockchain technology continues to evolve, so will its professional opportunities. The Blockchain is here with us to stay which means that Blockchain Expertise is to be in high demand for years and years to come. So, whether you are a techie or not, a career in Blockchain is a new and exciting opportunity worth exploring.

The combination of AI and blockchain holds many exciting potential applications. It’s worth remembering, though, that these technologies are still very young. Most of these hybrid applications will take years to develop or may not even develop at all. They’re certainly powerful ideas, but researchers and developers should take great care when working on such projects. The ethical implications of both blockchain and AI are massive, and the stakes are high for the economy, governance, and society as a whole.

If you are looking to learn these technologies then I would recommend you to enrol in Technogeeks training program under a working IT professional.

What kind of degree do I need to work with/on artificial intelligence?

AI is a sound career choice for a while now and as the adoption of AI in various verticals continues to grow, the demand for trained professionals to do the jobs created by this growth is also skyrocketing. Even though many AI pundits have prophesied that this technology will wipe out a massive amount of human jobs, there other pundits too who have said this will offer many unique and viable career opportunities. Therefore, if you are an AI enthusiast then be optimistic and prepare for a great career in AI.

Basic computer technology and math backgrounds form the backbone of most artificial intelligence programs. Entry-level positions require at least a bachelor’s degree while positions entailing supervision, leadership or administrative roles frequently require master’s or doctoral degrees. Typical coursework involves the study of:

·    Various levels of math, including probability, statistics, algebra, calculus, logic and algorithms.

·        Bayesian networking or graphical modelling, including neural nets.

·        Physics, engineering and robotics.

·        Computer science, programming languages and coding.

·        Cognitive science theory.

Candidates can find degree programs that offer specific majors in AI or pursue an AI specialization from within majors such as computer science, health informatics, graphic design, information technology or engineering.

Earning a bachelor’s degree in artificial intelligence can develop strong analytical thinking skills and problem-solving abilities that benefit graduates personally and professionally. Graduates may also develop proficiency in data mining and analysis that will open doors in a diverse range of industries. 

Having a bachelor’s degree in artificial intelligence may open the door to new and exciting career paths. Possible jobs include data scientist, machine learning specialist, or computer engineer. Someone who enjoys teaching could train others to develop and use artificial intelligence or may wish to teach at the secondary level or in higher education. Another alternative is working in sales for companies that market artificial intelligence applications to consumers. This is a growing field, so jobs may become available in a few years that do not exist today.

If you are not able or interested to do a degree in AI then you can also opt for an AI course from any reputed institution that will also help you to get into Artificial Intelligence field and boost your career in AI. So, I would recommend you to enrol in Technogeeks AI program, the course they offer is provided by working IT professionals and after the completion, of course, you will also get certification which is recognisable in the IT industry.

How Does AI Learn Things?

Unlike humans, AI has computational power that allows it to process copious amounts of data. As AI’s tasks continue to become more advanced, there’s also an increase in computational power necessary-which can become expensive. In order to avoid this need, AI was made to be a specific purpose learner. If AI gains the ability to relate data that is similar, it should be able to more efficiently process the data and understand it with a lower amount of power.

AI makes you look better in selfies, prevents blindness, and can even turn water into tastier beer. Tech giants and governments say we’re living in a golden age of AI. Roll out the self-driving cars!

AI is already used in multiple disciplines and industries. These devices are capable of reading, writing, speaking, seeing, and understanding. They are able to recognize emotions, play games, and even debate. Due to these capabilities, AI is often leveraged as a virtual assistant that can help plan meetings; as a tool to simplify the job hunting and hiring processes; and much more.

However, in order for this technology to be able to advance, it needs to be able to learn.

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn.

ANNs can be trained to recognise patterns in information - including speech, text data, or visual images - and are the basis for a large number of the developments in AI over recent years.

Conventional AI uses input to 'teach' an algorithm about a particular subject by feeding it massive amounts of information.  

Practical applications include Google's language translation services, Facebook's facial recognition software and Snapchat's image altering live filters.

The process of inputting this data can be extremely time consuming and is limited to one type of knowledge.

A new breed of ANNs called Adversarial Neural Networks pits the wits of two AI bots against each other, which allows them to learn from each other.

This approach is designed to speed up the process of learning, as well as refining the output created by AI systems.

If you want to learn artificial intelligence then you can enrol for one of the best resources i.e. Artificial Intelligence Course Technogeeks. This training is led by trainers who are AI experts. Besides, at the end of the training, they also awarded me an AI course completion certificate.

What is The Hot Research Topics in Data Science?

 The hot research topics in data science: -

Artificial Intelligence:

AI has become the mainstream technology for both small and large businesses, and it will bloom in the next few years. At present, we are at the initial stage of using artificial intelligence, but in 2021, we will see more advanced applications of AI in all fields. The reason AI is growing rapidly is that it allows enterprises to improve their overall business processes, and provides a better way of handling both customer and client’s data.

Though utilizing AI will still remain a challenge for many, as exploring the advancement of this technology is not that simple. In 2021, we will find more advanced apps developed with AI, Machine learning, and other technologies that can improve the way we work. Another trend that will take over the market is automated machine learning that will be helpful in transforming data science with better data management. So, you might need specialized training for executing deep learning.

Natural Language Processing:

Natural Language Processing (NLP) has made its way firmly into Data Science after huge breakthroughs in Deep Learning research.

Data Science first began as an analysis of purely raw numbers since this was the easiest way to handle it and collect it in spreadsheets. If you needed to process any kind of text, it would usually need to be categorized or somehow converted into numbers.

Yet it’s quite challenging to compress a paragraph of text into a single number. Natural language and text contain so much rich data and information - we used to be missing out on it since we lacked the ability to represent that information as numbers.

Huge advancements in NLP through Deep Learning are fuelling the full-on integration of NLP into our regular Data Analysis. Neural Networks can now extract information from large bodies of text incredibly quickly. They’re able to classify text into different categories, determine sentiment about a text, and perform analysis on the similarity of text data. In the end, all of that information can be stored in a single feature vector of numbers.

As a result, NLP becomes a powerful tool in Data Science. Huge datastores of text, not just one-word answers but full-on paragraphs, can be transformed into numerical data for standard analysis. We’re now able to explore datasets that are far more complex.

Data Science has become one of the growing fields in all industries, especially the IT industry. Thus, businesses adopting data science techniques and technologies must stay up-to-date with the latest trends. If you are looking to learn data science and want to work on data science trends then I would recommend you to take a look in Technogeeks Data Science program by working IT professionals.

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