How AI and Machine Learning Are Affecting the Computer Science Industr

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11/12/2019 How AI and Machine Learning Are Affecting the Computer Science Industry 1/3 How AI and Machine Learning Are Aecting the Computer Science Industry Swetha Nov 12 · 4 min read It’s no secret that artificial intelligence AI and machine learning are a big deal. By some estimates within 15 years automated algorithms and robots could take over approximately 40 percent of global jobs available to humans today. If you’re in the computer science or computer engineering industry news of this nature probably doesn’t affect you much. After all the jobs being replaced are often entry-level or those that require highly repetitive manual tasks. Computer science is a realm that would feasibly be in even higher demand with the advent of machine learning and AI. However there are some important changes already developing thanks to the higher demand and higher excitement for these technologies. How AI Startups and Companies Push for the Projects For starters AI and machine learning are hot topics in the realm of IT. Companies are clamoring for more machine learning solutions even if they don’t fully understand them pushing demand for new machine learning tools scripts and software to unprecedented new heights. For example it’s estimated that by next year roughly 20 percent of companies will have employees dedicated to monitoring and guiding neural networks and more than 10 percent of new IT hires in customer service will be responsible for writing scripts for chatbots one application of AI. There are going to be several side effects of this push. First novel projects that have the capacity to be even more impressive than conventional machine learning could be pushed aside as our most talented engineers and computer scientists chase the positions that are offering the most money or the widest range of opportunities. This means the

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11/12/2019 How AI and Machine Learning Are Affecting the Computer Science Industry 2/3 next truly original breakthroughs in computer science could be pushed back to make sure we explore this current trend to its fullest. Next the demand for lower-level IT experts is going to shrink making it harder to find entry-level positions and virtually impossible to sustain a career indefinitely unless you have some kind of niche skillset. AI’s Diversity Problems We’re also facing increasing consequences from the lack of diversity in AI and machine learning fields. AI experts are overwhelmingly white and male and the byproducts of an industry with an overwhelming majority are typically problematic unaware of how other populations are affected by their work. For example in the past facial recognition technologies have had trouble recognizing people with darker skin tones since these systems were developed by people who were mostly white. As interest in AI continues to grow it’s going to show up in more places. AI will be giving you search results choosing which news articles to show you gatekeeping access to your important files and personal information and possibly keeping you safe. And because the field will likely grow faster than the diversity problem can be solved it’s going to introduce many new headaches-both for consumers and for computer engineers and scientists trying to stay ahead of those problems. Why It’s Hard to See Where an Algorithm Goes Wrong Machine learning and AI also introduce a new layer of complexity that makes certain problems harder to solve-and it’s not just because they’re more complex than previous forms of coding. For the most part machine learning algorithms aren’t necessarily a set of instructions instead they’re designed as a vague learning process for a machine to follow. The machine collects data usually millions of examples of whatever it’s studying and gradually learns about the concept whether it’s recognizing faces in images or learning how to play Super Mario Bros. The problem with this is that while we can see the evidence of the machine getting closer and closer to achieving its goal or even surpassing human-level skills the developers can’t “see inside” the algorithm to determine which pieces of information led to a specific conclusion. In other words it’s incredibly hard to diagnose specifically where an algorithm goes right or wrong.

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11/12/2019 How AI and Machine Learning Are Affecting the Computer Science Industry 3/3 As AI and machine learning become more common this is going to become an increasingly complex problem for computer scientists and engineers to address requiring more insight higher-level skills and possibly entirely new approaches to developing AI in the first place. The Automation of Automation We also need to take seriously the possibility of one day automating the process of creating new machine learning or in an I n c e p t i o n-like twist new automation software. Such a multi-layered approach to computer science would open a new branch of study and require the creation of entirely new ways to look at problems in the computer industry. AI and machine learning aren’t merely passing fads though there may be even more novel computing breakthroughs on the near horizon. As a computer scientist or engineer it’s your responsibility to predict and adapt to the massive changes in store for the industry in the coming years as machine learning and AI become even more in demand. Machine Learning AI Ai Services Ai Technology Ai Development Company About Help Legal

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