AI refers to the ability of machines to demonstrate intelligence by perceiving, synthesizing, and inferring information, in contrast to the intelligence exhibited by non-human animals and humans. Examples of tasks that machines can perform with this ability include speech recognition, computer vision, translation between different languages, as well as other forms of input processing.
Machine learning (ML) is the process through which computers can perform tasks that they have not been explicitly programmed to do. It has simplified the creation of systems by enabling them to learn from large data sets, avoiding programming errors and logic issues. The future of machine learning appears bright, with ML being extensively applied in various fields, such as finance, banking, marketing, preventive healthcare, media, and medicine. The demand for machine learning has led to the open-sourcing of various ML projects, such as Tensorflow, CNTK, PaddlePaddle, MXNet, Caffe, and Torch.
The increasing use of artificial Intelligence and machine learning in various sectors necessitates an upgrade in hardware and software capabilities to accommodate the growth of AI applications. As a result, a plethora of AI ML Courses have sprung up online to train future engineers.
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Future of AI and Machine Learning
Cognitive computing and deep learning are two fields that will take center stage in AI and machine learning in the coming years. Cognitive computing will utilize meticulously organized and broken-down data to instruct and assess intelligent machines. On the other hand, deep learning will use unsupervised learning from unstructured or unlabeled data to train the system. The two fields will collaborate to perform mainstream tasks in various sectors, such as scientific research, medicine, healthcare, self-driving cars, and ultimately, sentient computing machines.
The 2017 edition of the Hype Cycle for Emerging Technologies places machine learning in both the PIE and ‘AI Everywhere’ sections, which indicates an enhanced popularity as well as a risk of our expectations outweighing its potential. Eventually, the discrepancies and misjudgments will become apparent as we gain a more thorough understanding of this topic. The future of machine learning is promising, and the demand for it will continue to rise, leading to significant innovations in the field. Thus, AI ML experts have huge opportunities awaiting them in the coming years. But what exactly are these opportunities? And in what specific sectors do AI ML experts have high demand? Let us look at this in detail now.
Future Demand for AI ML Engineers
Let us discuss some of the top jobs employing AI ML experts and their future scope in the coming years.
Data Scientists
Data scientists are responsible for analyzing and processing data. Their work is crucial to the development of machine learning applications. IBM has projected that the demand for data engineers, developers and scientists will surge by 28% come 2020. A postgraduate degree in data science, combined with a few years of experience, can make you an attractive candidate for employers. If you are able to secure the right role, not only will you receive an impressive starting salary of USD 300,000 but could also potentially benefit from company stocks as your career advances into higher-level positions.
AI ML Engineers
For machine learning/AI engineers, it is imperative to be well-versed in programming languages and possess a deep comprehension of algorithms in order to educate machines. The need for AI programmers in fields such as cybersecurity, translation systems medicine, speech-to-text, healthcare, and natural language processing, will be unparalleled in the next ten years. The industry has a talent shortage, and the competition for professionals is fierce. Those with a postgraduate degree in AI or machine learning and practical experience with AI projects can enter this profession.
Data Labelling Specialists
Data labeling specialists organize raw data for machine ingestion. Without their involvement in data curation, machines would not be able to process or learn from structured information. This role is therefore essential for providing clean and organized data that enable machine learning. Hardware specialists are responsible for designing and building chips that are more efficient, stronger, dependable, and more durable. With the rapid development of technology, employers are increasingly in search of professionals who possess experienced knowledge in EC and a Master’s degree specializing in chip design.
Data Security Analysts
Data security analysts will vigilantly protect digital information from unauthorized manipulation, theft, and destruction. They will be responsible for building, maintaining, and improving security systems, ensuring authentication and authorization, and developing decryption and encryption systems. To become an IT security manager, having a postgraduate degree in security and the CISSP credential are vital prerequisites.
Conclusions
Future opportunities in the fields of AI and ML will create a demand for various professionals. The workforce will require specialists such as data scientists, AI/machine learning engineers, data labeling specialists, AI hardware specialists, and data security analysts. These professions will be unpredictable and demanding, and only those who adapt quickly will stay in the field. Despite the fact that Artificial Intelligence can analyze huge amounts of data, human discernment will always remain indispensable. The requirement for skilled workers to develop and maintain these systems will remain. The need for human judgment is demonstrated in the failure of automated cars.
