Artificial intelligence has seen a rapid growth in the domains it found relevance in. From smart refrigerators to self-driving cars – everything is a result of sophisticated artificial intelligence algorithms. Who’s responsible for it? The Artificial Intelligence Engineers.
Artificial intelligence is thought to be the same as machine learning, but in reality, the latter is a subset of the former. AI is a broad field with diverse applications, but also one of the most challenging domains to work in. Artificial Intelligence aims to impart machines the ability to mimic humans in almost every aspect – which is way more difficult than it sounds. Machines inherently are dumb devices and require a lot of data, computing power, and efforts to learn.
The most successful AI professionals often share common characteristics and love for machines that allow them to bloom in their career. Working with AI requires an extremely analytical and logical thought process, and the ability to solve the most challenging problems most cost-effectively and efficiently. Artificial intelligence engineers are expected to have a clear foresight about the technological innovations that translate to state-of-the-art programs that allow businesses to remain competitive.
Furthermore, AI specialists also need to armed with technical skills required to design, develop, maintain, monitor, and repair their systems and programs. Finally, the AI professionals must be proficient in translating highly technical information in ways that are comprehensible to even those from a non-technical background. They need to work in collaboration with the rest of the organisation to produce the most insightful results.
Alright, now that we’re on the same page regarding the job roles of an Artificial Intelligence Engineer, let’s look at some critical skills that any AI professional must possess.
Basics of computer science and maths 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.
Any aspiring AI engineer should be comfortable with:
- Various level of math, including probability, statistics, algebra, and calculus.
- Bayesian networking or graphical modelling, including neural nets.
- Physics, engineering, and robotics.
- Computer science, programming languages, and coding.
- Cognitive science theory.
The field of Artificial Intelligence has been continuously growing and has given rise to various new technologies that these AI developers/engineers consistently work on. Let’s see what they are:
Natural Language Processing and Text Analytics
NLP uses and supports text analytics. NLP helps in understanding any sentence said in a natural language regarding structure, sentiment, intent, and meaning through statistical methods. NLP finds extensive use in fraud detection and security, a wide range of automated assistants (Siri, for instance), and applications for mining of unstructured data.
From simple chatbots to advanced systems that can seamlessly interact with humans, all of this has been made possible because of AI and Artificial Intelligence engineers. The usage of these chatbots and virtual agents is increasing as organisations realise the importance of chatbots for customer service and support.
Hardware needs to become much more accommodating as AI, and related technologies grow. And what does that mean?
Graphics processing units (GPU) and appliances specially designed and developed to run AI-oriented computational jobs efficiently. They’re having a massive impact on Deep Learning applications. Some vendors developing such GPUs include Cray, Google, IBM, Intel, and Nvidia.
Natural Language Processing
Biometrics deal with the identification, measurement, and analysis of physical aspects of the human body. It allows much more natural interactions between humans and machines taking care of interactions related to touching, seeing, speaking and recognizing body language.
Deep Learning Platforms
Deep learning platforms take artificial intelligence and machine learning to a whole new level by working with advanced neural networks with various abstraction layers. This technology mimics the human brain by processing data and creating patterns that aid in decision making.
Now, let’s walk you through some myths and misconceptions – we’re sure you, too, have some of these in mind. Let’s together bust them!
Myth #1: AI thinks exactly like a person – it can solve all the problems that humans can.
There is no such thing as general intelligence in AI yet, and perhaps we don’t need it either. If anything, today AI focuses more on teaching a lemur how to get food and not about letting a chimpanzee figure it out for themselves. Most of the AI functions are developed for a particular purpose, such as natural language processing (NLP), image recognition, search engines, gaming, predictions, or specific features in self-driving cars. This often brings higher business value than general intelligence. A specialist is always preferred over a generalist.
Myth #2: AI is the same thing as Machine Learning or Deep Learning.
AI is often misinterpreted for ML, Deep Learning, or even Cognitive Processing. However, the truth is that ML is a part of AI wherein feeding data regularly trains the machine. Like we mentioned earlier, AI is broader than that and forms the superset of the technologies we mentioned.
Myth #3: Artificial intelligence engineers just develop the system once, it keeps learning by itself then.
If only! Even machine learning, a subset of AI, remains extremely difficult to implement. Of course there are easier and tougher challenges, but in general, getting these algorithms to fit your business needs is a task in itself. Often the algorithms are easy to understand, but the challenge is in selecting the right algorithm for the problem and presenting the data to the algorithm in the correct way. This requires a comprehensive knowledge of the problem as well as a thorough understanding of the capabilities and constraints of the algorithms and models. Complicating matters further is the fact that machines require the correct amount of training to get artificially intelligent.
This training needs accurate data in as pure a form as possible. Further, the data that is used to train is extraordinarily dynamic and gets stale if not used at the right time. So, artificial intelligence engineers also need to perform iterations after iterations on their system to make sure it works seamlessly with such a dynamic data.
Myth #4: AI algorithms can magically create intelligent systems. The quality of data that are fed is irrelevant for AI.
AI is anything but “load and go.” AI won’t be of much help if you have an extremely broad or unprocessed data. Such data is indigestible for any system and will often result in erroneous results. Rather than ingesting anything and everything, an AI engineer needs to carefully curate the data and make sure it’s of the highest possible quality. An algorithm is nothing but a program, and a program requires data to work with. The better the data, the better the results.
Myth #5: AI is an extremely new field.
John McCarthy coined the term “artificial intelligence” back in 1956 and then went on to define the domain for more than five decades. So, although the concept AI is not so new, it is much more widespread in the world today.