Building bridges between neuroscience and AI
Acquiring solid knowledge is the cornerstone of a career in AI. According to the Merriam-Webster dictionary, artificial intelligence is defined as "the capability of computer systems or algorithms to imitate intelligent human behavior."
It is a significant challenge to create AI solutions that can mimic the complexity of the human brain, composed of neurons estimated to number around 100 billion. Neurons communicate with each other and transmit important information through synapses via electrical impulses. Therefore, a basic understanding of neurobiology is essential in pursuing success - making machines "think" similarly to humans. In the case of human beings, "data" is inputted into the brain from childhood. We learn different languages, social behaviors, cultures, and so on. Humans mimic their parents and the environment around them. Data is processed and encoded in the brain. In the case of artificial intelligence, the set of necessary data is inputted and loaded for the needs of a particular model. To "teach" and test these models, we need extensive knowledge.
Educate to elevate: The path to excelling in AI careers
AI engineers are creators and need various skills to turn their vision into a cyber reality. To become a highly skilled professional with diverse abilities and expertise, and to teach AI how to “think”, one requires solid academic knowledge, as well as strong problem-solving skills, an analytical mind, the ability to work in a team, and a lot of practice. Here are the same ideas about directions in which we can develop to gain the necessary skills.
- Bachelor's Degree
A bachelor's degree in computer science, mathematics, or a related field is usually essential. Courses in data structures, algorithms, linear algebra, calculus, statistics, and probability provide the mathematical foundations required in artificial intelligence. Programming languages such as Python, R, and Java are crucial, with Python being the most prevalent in the AI community.
- Specialized Courses and Certifications
Postgraduate certificates and online courses can expand knowledge in specialized areas of artificial intelligence, such as machine learning, neural networks, and deep learning. Platforms such as Coursera, edX, and Udacity offer courses developed by leading universities and companies.
- Master's and Ph.D. programs
A master's or doctoral degree in artificial intelligence (AI) is highly beneficial for advanced positions in research and development. Specializing in AI during these programs allows candidates to engage in in-depth research, contributing to academic and practical knowledge. Doctoral dissertation topics often include innovative applications of AI or enhancements to existing algorithms.
Technical Skills as a base for building your career in AI
Światowa Organizacja Zdrowia wyróżnia 3 sygnały wypalenia zawodowego, na które powinieneś zwrócić uwagę:
1. Ekstremalny Cynizm
Masz wrażenie, że Twoje negatywne odczucia związane z pracą wypierają te pozytywne? Czujesz, że Twoja praca nie jest doceniana, jest beznadziejnie i nie da się nic z tym zrobić? Cynizm jest w rzeczywistości czerwoną flagą wypalenia zawodowego. Jeśli postrzegasz (lub ktoś z Twojego zespołu postrzega) pracę jako pozbawioną większego sensu. Najwyższy czas zastanowić się, czy to nie oznaka większego problemu.
Czym przejawia się cynizm?
- Utratą sensu.
- Ciągłą frustracją.
- Poczuciem rozczarowania.
- Brakiem radości z wykonywanej pracy.
To może mieć wpływ na Twoją wydajność w pracy i wydajność innych osób w zespole. Przykład: jeśli pracujesz w dziale wsparcia IT, możesz odpowiedzieć na ticket linkiem do Let Me Google That For You. I włożyć minimalny wysiłek w pomoc, bo przecież nic się nie liczy. W rezultacie pozostałe osoby z Twojego teamu mogą nie chcieć poprosić Cię o pomoc, bo wiedzą, jakiej odpowiedzi mogą się spodziewać.
2. Przewlekłe wyczerpanie
Czujesz, że ciągle brakuje Ci energii, a Twoja praca nie jest tak efektywna? Masz problem z zebraniem się do pracy. Nie jesteś w stanie wrzucić najwyższego biegu i czujesz się wyczerpany pod koniec dnia? Każdy z nas może mieć gorszy dzień. To zupełnie normalne. Jednak w tym przypadku odpoczynek i krótka przerwa nie zdają egzaminu. Osoby z przewlekłym wyczerpaniem czują się tak, jakby cała energia wyparowała i (co gorsza) nie mogą jej odzyskać.
Oto kilka oznak wyczerpania na poziomie wypalenia:
- Brak energii.
- Zerowa motywacja.
- Niezdolność do koncentracji.
- Poczucie ogromnej ulgi na weekend.
- Strach przed wzięciem wolnego, aby odpocząć.
3. Negatywna samoocena
Branża IT charakteryzuje się wysokimi wynikami, dlatego poczucie, że odstajesz od reszty zespołu i nie dajesz sobie rady, może być szczególnie niszczące. Przecież nikt z nas nie lubi czuć się niekompetentny. Nie chcemy też być kulą u nogi dla reszty naszych współpracowników. Czuć, że stoimy w miejscu. Jeśli masz wrażenie, że pracujesz całe dnie, ale nic nie osiągasz lub wszystko, co robisz, jest złe. Może to być oznaką wypalenia zawodowego.
Jak mają się do tego badania?
Statystyki są przerażające i mówią same za siebie. Badanie „The State of Burnout in Tech 2022” przeprowadzone na ponad 30.000 specjalistach IT wykazało, że:
- 51% czuje, że osiąga mniej, niż powinno.
- 56% mężczyzn i 69% kobiet nie potrafi się zrelaksować po zakończeniu dnia pracy.
- 43% czuje się niezaangażowanych w swoją pracę, a 27% nie widzi w niej żadnej wartości.
It takes more than just a formal education to excel in artificial intelligence. One must also have diverse technical skills, such as proficiency in programming languages, data analysis, natural language processing (NLP), machine learning algorithms, and computer vision. These skills are critical for tackling the complex and ever-evolving challenges of AI. Thus, if you aspire to build a successful career in this field, acquiring these technical skills and remaining updated with the latest advancements in the industry is essential. How to reach this goal? Read the following.
- Programming proficiency
Regardless of what AI career you dream of, proficiency in programming languages is crucial. It can help you become a machine learning engineer, algorithm developer, computer vision engineer, or SLAM engineer. The most used programming languages are Python, R, Java, C++, and JavaScript. However, Python still remains the most used language thanks to its vast libraries and frameworks (e.g., TensorFlow, PyTorch, and Scikit-learn).
Fluency in AI frameworks and libraries is necessary for developing and implementing AI models, and mastery of frameworks is often required at work.
The most popular frameworks and libraries are TensorFlow, PyTorch, Keras, Scikit-Learn, Theano, Caffe, MXNet, Microsoft Cognitive Toolkit (CNTK), Apache Mahout, LangChain, OpenNN, PyBrain, IBM Watson, and Deeplearning4j (DL4J).
- Machine Learning and Neural Networks
It is crucial to have a solid understanding of machine learning, including supervised and unsupervised learning, reinforcement learning, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs).
Practice makes perfect
One of the most valuable ways to supplement your studies, courses, or tutorials is by gaining practical experience through personal or academic projects. Engaging in such projects allows you to use the educational concepts and theories learned in a real-world setting. It gives you a deeper understanding and appreciation of the subject matter. How can it help in the work environment?
- Projects and portfolio development
Presenting your portfolio showcasing successful projects and solutions improves your employment prospects. Projects may include image recognition systems, natural language processing applications, or predictive models for various industries. These projects tell potential employers or clients a lot about your skills and knowledge.
- Internships and research
Internships at companies specializing in artificial intelligence can be a road to gain experience and make valuable contacts in the industry. Participating in research, whether at a university or in industry, helps budding AI engineers stay on top of the latest developments in the field.
- Contests and hackathons
Engaging in AI-related competitions and hackathons can provide real-world experience in solving AI problems. Platforms like Kaggle unite the most significant AI and ML communities and offer the opportunity to solve real business problems, develop innovative solutions, and communicate.
Ethical sides of AI solutions
It is essential to always consider the ethical implications of our actions and decisions while maintaining a commitment to continuous learning to improve and grow. This also applies to AI engineers who must be aware of the moral implications of AI technologies. Understanding considerations and biases in AI and ensuring privacy and security are integral to responsible AI development.
Conclusion
Becoming an AI engineer requires a robust educational background, technical proficiency, practical experience, and a commitment to continuous learning and ethical practice. For those aspiring to enter this dynamic field, the journey involves both deep theoretical study and hands-on application, a challenging yet rewarding combination that paves the way to a promising career in AI.
Sources
Optimizing AI For Businesses: Transforming Industries. https://v2websolutions.com/optimizing-ai-for-businesses/
AI Predictive Analytics Can Revolutionize Retail - TechBullion. https://techbullion.com/ai-predictive-analytics-can-revolutionize-retail/
How to get a Job in Artificial Intelligence. https://learnsic.com/blog/how-to-get-a-job-in-artificial-intelligence
Best Machine Learning Development Services Company. https://pattemdigital.com/machine-learning/
The Power of AI/ML in Surveys - PxidaX. https://pxidax.com/technology/the-power-of-artificial-intelligence-and-machine-learning-in-surveys/
AI Predictive Analytics Can Revolutionize Retail - TechBullion. https://techbullion.com/ai-predictive-analytics-can-revolutionize-retail/
The Top 16 AI Frameworks and Libraries: A Beginner's Guide
https://www.datacamp.com/blog/top-ai-frameworks-and-libraries
