February 1, 2020 Comment 0 Top influential AI skills to target in 2020 Artificial intelligence in 2020: a trustable year! The AI market is deemed to reach USD70 billion in the present year, thus causing a drastic effect on the government market, consumer, enterprise globally. With market uproars, it is certain obstacles are bound to intercept. However, the effects of artificial intelligence are poised to have huge potential in democratizing expensive services, or boost poor customer service, or assist during medical breakthroughs, and even lightened the work of the overburdened workforce. If you’re a tech optimist who believes in creating a world where man and machine can come closer and work together with humans, then you better need to possess mandatory AI skills. Based on this report, “2020 Workplace Learning Trends Report: The Skills of the Future,” you will come across how AI is reshaping the world and what are the skills that are a must-know for upcoming AI professionals. The report also states that the investment funds that are managed by AI are accounted for 35% of the stock market in America. Finance machines will rise says a recent article in ‘The Economist.’ Trending by the industry’s insights and job trends, these are the skills AI professionals need to master in 2020: Machine learning People often use the terms artificial intelligence and machine learning interchangeably, both being entirely different from each other. There’s a lot of confusion between both these terms, however, let us briefly understand what exactly is machine learning. Machine learning uses algorithms that obtain knowledge and skill through experience and without human intervention. It relies on big data sets that remind the data to find common patterns. For instance, if you provide machine learning programs with a lot of data on skin conditions and tell them what these conditions signify, these algorithms can easily mine the images (data) and help in analyzing the skin conditions even in the future. Now the algorithms will compare the images with the previous image data and try to identify the pattern that exists between them that have similar kind of patterns. However, if the algorithm is given a new image of a skin condition and the condition is unknown in the future as well. The algorithm will take the image to analyze it with the past and the present situation, thus the prediction of the condition will remain improper since one needs to feed new data for the algorithm to learn in order to predict what the condition is. However, AI will only learn by obtaining knowledge and learn how to apply these knowledges. Artificial intelligence helps to train computers aiming at a result that could provide a better outcome than a human can do. Python As the phrase is used, ‘orange is the new black’ so are AI and ML becoming the new black of the IT industry. With the extensive expansion of volumes of data, machine learning and artificial intelligence are used progressively for processing as well as analysis. To be honest, a human brain can function and analyze huge amounts of data but a human brain is restricted by limitations and can only contain a certain amount of processing or analyzing. AI in that aspect has a huge capacity and requires no limitation. Now Python plays a major impact on AI and machine learning as we undergo an upsurge for AI engineers. According to the latest trend search on Indeed, Python is said to be the most popular language for AI and ML, as stated by Jean Francois Puget, from the machine learning department in IBM. The how’s and why’s? Python offers a low entry barrier for data scientists to effectively use Python without wasting much effort in learning the language. It has a great choice of library ecosystem, no wonder why everybody loves Python. For instance, Pandas for producing high-level data structure or data analysis, Matplotlib to create 2D plots and histograms, or Keras that is widely used for deep learning. Python as a language is flexible and is a great choice for machine learning. AI professionals can easily use Python along with other programming languages to achieve their target. Python offers easy readability for every developer, thus making it easy to understand the codes of their senior, change or develop a new one whenever needed. React (web) If you’re a web developer thriving to enter the AI world, here’s a great chance for you. You can now build sweet AI apps using React.js. These offers web developers a new platform to bridge the gap between web developers and professionals who are getting trained in AI skills. A web developer can now easily build apps leveraging artificial intelligence that can learn from experiences or learns to react to the user’s inputs like facial expression, etc. Angular If one isn’t aware, Google AI is build using Angular. Building a chatbot from scratch using a Dialog flow conversation platform, previously known as API.ai can be challenging. NLP (natural language processing) could be tough to deal with in machine learning. Docker Docker is now used in every field of the software industry. Confused with the term? Well, Docker is nothing but a DevOps tool. Docker can also be called as an exploiter tool exploiting operating-system-level virtualization that basically helps in developing and delivering software in packages that are called containers. Though this may sound complicated, you need to simply know that Docker is a complete environment that offers you a platform to build as well as deploy software. In a nutshell, one may say that Docker can be employed for varied phases of the machine learning development cycle like data aggregation, data-gathering, model training, predictive analysis, and application deployment, etc. Tech professionals such as DevOps engineers and AI engineers will need to supercharge their skill set in 2020. Till the time this knowledge gap persists, we will continue to see talent shortage in the job market. Michael LyammWriter, Guest Blogger, Social Media Strategist. Experienced curator in the fields of Technology-AI, Block chain, Data Science, Marketing and Startups.