This will allow a wider range of organizations to take advantage of machine learning … I won't sell or share your email. Terms of Use - Machine learning is a powerful new technology – and it's something that a lot of companies are talking about. This Week in Machine Learning: AI and Google Search, LO-shot Learning, Dangers of AI, New Deep Learning Models Posted October 27, 2020 It’s been two weeks since our weekly roundup. Z, Copyright © 2020 Techopedia Inc. - Machine Learning Risks are real and can be very dangerous if not managed / mitigated. One of the worst outcomes in using machine learning poorly is what you might call “bad intel.”. Tech Career Pivot: Where the Jobs Are (and Aren’t), Write For Techopedia: A New Challenge is Waiting For You, Machine Learning: 4 Business Adoption Roadblocks, Deep Learning: How Enterprises Can Avoid Deployment Failure. K    This article reflects on the risks of “AI solutionism”: the increasingly popular belief that, given enough data, machine learning algorithms can solve all of humanity’s problems. View all questions from Justin Stoltzfus. The dangers are enhanced by the fact that many machine learning methods like neural networks are very complex and hard to interpret. Your accuracy goes into the toilet. The dangers of trusting black-box machine learning Two types of black-box AI. Like my friend Gene De Libero says: ‘Test, learn, repeat (bruises from bumping into furniture in the dark are OK).”. but there are some very good arguments about bias that are worth the time to read. When you think about applying machine learning, you have to choose the right fitting. Most of the objections they put forth pretty much echo the arguments here. What are some of the dangers of using machine learning impulsively? While i’m not a fan of up-sampling data from high to low granularity, but it made sense for this particular modeling exercise. One of the worst outcomes in using machine learning poorly is what you might call “bad intel.” This is a nuisance when it comes to ironing out the kinds of decision support systems that machine learning provides, but it's much more serious when it's applied to any kind of mission-critical system. He called up the manager of the data scientist and read her the riot act. Data scientists need to be just as good at communicating as they are at data manipulation and model building. However, while 20% might consider the automation of jobs to be one of the dangers … […] few weeks ago, I wrote about machine learning risks where I described four ‘buckets’ of risk that needed to be understood and mitigated […], Really interesting discussion. O    This conclusion can be tested and overridden, though, if a user’s nationality, profession, or travel proclivities are included to allow for a native visiting their home country or a journalist or businessperson on a work trip. Then, your boss takes a look at it and interprets the results in a way that is so far from accurate that it makes your head spin. It’s not clear to me, though, that any of these risks are unique to big data or techniques used to analyze big data. D    So, if we input a set of data—such as that from a GPS system—along with injury data across a season, the software will try to create a model that allows it to predict which players got injured. Machine learning refers to the process by which a computer system utilizes data to train itself to make better decisions. I see this all the time in the financial markets when people try to build a strategy to invest in the stock market. I’ve had discussions with colleagues about whether you can ever have too much data. I    in Information Systems in 2014 with a dissertation titled “Analysis of Twitter Messages for Sentiment and Insight for use in Stock Market Decision Making”. And Arnold Schwarzenegger appears, in undoubtedly the easiest role of his career. 6 Cybersecurity Advancements Happening in the Second Half of 2020, 6 Examples of Big Data Fighting the Pandemic, The Data Science Debate Between R and Python, Online Learning: 5 Helpful Big Data Courses, Behavioral Economics: How Apple Dominates In The Big Data Age, Top 5 Online Data Science Courses from the Biggest Names in Tech, Privacy Issues in the New Big Data Economy, Considering a VPN? Read next This is the easiest method to create a social media marketing strategy. E    For example, assume you are building a model to understand and manage mortgage delinquencies. Richard Welsh explores some of the issues affecting artificial intelligence. Vendor’s Expertise and Exclusive Focus on Healthcare. Machine learning refers to the process by which a computer system utilizes data to train itself to make better decisions. Viable Uses for Nanotechnology: The Future Has Arrived, How Blockchain Could Change the Recruiting Game, C Programming Language: Its Important History and Why It Refuses to Go Away, INFOGRAPHIC: The History of Programming Languages, 5 SQL Backup Issues Database Admins Need to Be Aware Of. Just realize that bias is there and try to manage the process to minimize that bias. Supervised machine learning requires less training data than other machine learning methods and makes training easier because the results of the model can be compared to actual labeled results. . The second risk area to consider for machine learning is the data used to build the original models as well as the data used once the model is in production. Forget what you may have heard. Suppose machine learning algorithms do not make precise and targeted choices – and then executives go along blindly with whatever the computer program decides! Deepfakes Expose Societal Dangers of AI, Machine Learning Deepfake videos are enabled by machine learning and data analytics, and at best can be a form of entertainment. The data gathering abilities of AI also mean that a timeline of your daily activities can be created by accessing your data from various social networking sites. Buy-in for good opportunity cost choices can be an issue. Discussions about AI often focus on its positive impacts for society while disregarding the more difficult and less-popular idea that AI could also potentially be dangerous. His research interests are currently in the areas of decision support, data science, big data, natural language processing, sentiment analysis and social media analysis.In recent years, he has combined sentiment analysis, natural language processing and big data approaches to build innovative systems and strategies to solve interesting problems. He currently runs his own consulting practice focused on helping organizations use their data more efficiently. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it. As machine learning becomes increasingly valuable and the technology matures, more businesses will start using the cloud to offer machine learning as a service (MLaaS). For any machine learning model, we evaluate the performance of the model based on several points, and the loss is amongst them. The simplest way to explain overfitting is with the example of a two-dimensional complex shape like the border of a nation-state. All of these problems–bias, bad data, overfitting, wrong interpretations–also inhere, potentially, in smaller data sets. W    Deep Reinforcement Learning: What’s the Difference? Y    How does Occam's razor apply to machine learning? Participation-washing could be the next dangerous fad in machine learning. It all revolves around the basic idea of providing machine with the ability to take autonomous decisions, … If you asked 100 data scientists and you’ll probably get as many different answers of what the ‘big’ risks are – but I’d bet that if you sit down and categorize them all, the majority of them would fall into these four categories. They build a model strategy and then tweak inputs and variables until they get some outrageous accuracy numbers that would make them millionaires in a few months. Here's why. It's like trying to put a massive high-horsepower engine in a compact car – it has to fit. ... Machine learning was able to identify and predict where the lead pipes were, so it reduced the actual repair costs for the city. This isn’t a bad categorization scheme, but I like to add an additional bucket in order to make a more nuanced argument machine learning risks. Richard Welsh explores some of the issues affecting artificial intelligence. Why is machine bias a problem in machine learning? We need to get one more thing out of the way … Latest technologies like facial recognitioncan find you out in a crowd and all security cameras are equipped with it. #    Feel free to contact me to see how I might be able to help manage machine learning risks within your project / organization. It’s a way to achieve artificial … Artificial intelligence could soon be indispensable to healthcare, diagnosing … The Rise Of Machine Learning And The Risks Of AI-Powered Algorithms Subscribe Now Get The Financial Brand Newsletter for FREE - Sign Up Now Back in the Old Days, you used to have to hire a bunch of mathematicians to crunch numbers if you wanted to extrapolate insights from your data. I agree with you David. Everyone wants to ‘do’ machine learning and lots of people are talking about it, blogging about it and selling services and products to help with it. The dangers of letting algorithms make decisions for you ... To end this dilemma, researchers working on machine learning advocate greater transparency and providing explanations … I know everyone ‘needs’ to be doing machine learning / AI but you really don’t need to throw caution to the wind. For example, If you start with that big project and realize that […], Eric D. Brown, D.Sc. In addition to the bias that might be introduced by people, data can be biased as well. There is no earthly limitations to the kind of blessings that comes in the form of machine learning. If you'd like to receive updates when new posts are published, signup for my mailing list. Machine learning isn’t some new concept or study in its infancy. A quantitative analyst estimates that some machine learning strategies may fail up to 90 percent when tested in a real-life setting… Machine learning can easily consume unlimited amounts of data with timely analysis and assessment.This method helps review and adjusts your message based on recent customer interactions and behaviors. Turns out he had missed that the output was showing quarterly sales revenue instead of weekly revenue like he was used to seeing. Once a model is forged from multiple data sources, it has the ability to pinpoint relevant variables. Furthermore, machine learning is prone to being stuck in feedback loops, which can end up perpetuating bias. Because the training data used by machine learning will include fewer points, generalization error can be higher than it is for more common groups, and the algorithm can misclassify underrepresented populations with greater frequency—or in the loan context, deny qualified applicants and approve unqualified applicants at a higher rate. This can’t be further from the truth. Resulting problems have to do with efficiency – if you do run into problems with overfitting, algorithms or poorly performing applications, you're going to have sunk costs. Learn about your data and your businesses capabilities when it comes to data and data science. However, there are serious implications to note when using a machine learning system to make risk assessments. 10 min read. He told her the reports were off by a factor of anywhere from 5 to 10 times what it should be. By Francois Swanepoel. Automation: The Future of Data Science and Machine Learning? Cathy O’Neill argues this very well in her boo… Hopefully its been informative. Even today, it is possible to track you easily as you go about your day. Take note of the following cons or limitations of machine learning: 1. - Renew or change your cookie consent, Optimizing Legacy Enterprise Software Modernization, How Remote Work Impacts DevOps and Development Trends, Machine Learning and the Cloud: A Complementary Partnership, Virtual Training: Paving Advanced Education's Future, IIoT vs IoT: The Bigger Risks of the Industrial Internet of Things, MDM Services: How Your Small Business Can Thrive Without an IT Team. A machine learning vendor that’s exclusively … The combination of poor ML outcomes and poor human oversight raises risks. Machine learning models are built by people. The inputs are tweaked to give the absolute best output without regards to variability of data (e.g., new data is never introduced). Justin Stoltzfus is a freelance writer for various Web and print publications. Cathy O'Neil has a phrase for these types of potentially biased machine Learning Systems in life changing roles. You grab some credit scoring data and build a model that predicts that people with good credit scores and a long history of mortgage payments are less likely to default. Model output is misinterpreted, used incorrectly and/or the assumptions that were used to build the machine learning model are ignored or misunderstood. You need domain experts and good data management processes (which we’ll talk about shortly) to overcome bias in your machine learning processes. I get it…machine learning can bring a lot of value to an organization – but only if that organization knows the associated risks. Machine learning isn’t some new concept or study in its infancy. A model provides estimates and guidance but its up to us to interpret the results and ensure the models are used appropriately. We’re Surrounded By Spying Machines: What Can We Do About It? M    Bias that’s introduced via data is more dangerous because its much harder to ‘see’ but it is easier to manage. Early statistical models in those days paved the way for today’s modern artificial intelligence.. On the contrary, while today’s machine learning … Like many things involving artificial intelligence, there’s a bit of confusion surrounding... Explainability vs interpretability. How Can Containerization Help with Project Speed and Efficiency? Additionally, he is the Chief Information Officer of Sundial Capital Research, publisher of SentimenTrader, Eric received his Doctor of Science (D.Sc.) In fact, China is currently working on a Social … For any machine learning model, we evaluate the performance of the model based on several points, and the loss is amongst them. You spend a lot of time making sure you have good data, the right data and the as much data as you can. He was furious and shot off an email to the data team, the sales team and the leadership team decrying the ‘fancy’ forecasting techniques declaring that it was forecasting 10x growth of the next year and “had to be wrong!”. If you only use six or eight data points, your border’s going to look like a polygon. We can then feed in additional information, such as the next season’s injury data, and the co… Doesn’t matter the size of the data, these risks are all valid. Privacy Policy This happens all the time. Bias exists and will be built into a model. N    V    C    The model was built on the assumption that all data would be rolled up to quarterly data for modeling and reporting purposes. Machine learning as a service will become more common. More of your questions answered by our Experts, The Promises and Pitfalls of Machine Learning. Some folks might call ‘lack of model variability’ by another name — Generalization Error. You’re going to be famous. With data, you can have many different risks including: You spend weeks building a model. U    B    A    People have biases whether they realize it or not. But…what if a portion of those people with good credit scores had mortgages that were supported in some form by tax breaks or other benefits and those benefits expire tomorrow. What happens to your model if those tax breaks go away? It can be hard to change course and adapt and maybe get rid of machine learning programs that aren't going well. However, it's not without its problems in terms of implementation and integration into enterprise practices. Machine learning, also known as Analytics 3.0, is the latest development in the field of data analytics. Deloitte splits machine learning risks into 3 main categories: Data, Design & Output. One more thing about output interpretation…a good data scientist is going to be just as good at presenting outputs and reporting on findings as they are at building the models. You might have really clunky applications with extensive problems, and a bug list a mile long, and spend a lot of time trying to correct everything, where you could've had a much tighter and more functional project without using machine learning at all. We can then feed in additional information, such as the next season’s injury data, and the co… Editorial: There are dangers of teaching computers to learn the things humans do best – not least because makers of such machines cannot explain the knowledge their creations have acquired X    F    You want enough data points to make the system work well, but not too many to mire it down in complexity. The output of the model was provided to the VP of Sales who immediately got angry. Machine learning (ML), a fundamental concept of AI research since the field's inception, is the study of computer algorithms that improve automatically through experience. Similar approaches should be taken in other model building exercises. You can read some of his research here: Eric D. Brown on ResearchGate. Again – this is a simplistic example but hopefully it makes sense that you need to understand how a model was built, what assumptions were made and what the output is telling you before you start your interpretation of the output. happens. You do everything right and build a really good machine learning model and process. Here are some of the biggest pitfalls to watch out for. That brings us to another major problem with machine learning inherently – the overfitting problem. In the world of investing, this over-optimization can be managed with various performance measures and using a method called walk-forward optimization to try to get as much data in as many different timeframes as possible into the model. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, it can be (and has been) a very large issue, How sure are you that the economic data is real, Accuracy and Trust in Machine Learning - Eric D. Brown, Artificial intelligence: Examples of how to start successfully | Techthriller | Latest Tech News, Artificial Intelligence: Examples of How to Start Successfully ~ QCM Technologies, By chasing the big might, you might just ignore the small, Customer Service is made up of the small things, technology consultant, investor and entrepreneur. To address potential machine-learning bias, the first step is to honestly and openly … A machine-learning algorithm may flag a customer as high risk if he or she starts to post photos on social media from countries with potential terrorist or money-laundering connections. Just realize that bias is there and try to manage the process to minimize that bias. This particular model was built on quarterly data with a fairly good mean error rate and good variance measures. Weapons of math destruction. In some cases, the machine learning might work right on a fundamental level, but not be entirely precise. Techopedia Terms:    Machine Learning has a … The dangers of letting algorithms make decisions for you ... To end this dilemma, researchers working on machine learning advocate greater transparency and providing explanations for training models. An organization had one of their data scientists build a machine learning model to help with sales forecasting. Forget what you may have heard. I’d put money on the fact that your model isn’t going to be able to predict the increase in numbers of people defaulting that are probably going to happen. Looking at all the statistics, it was a good model. Not too long ago, it was considered state of the art research to make a computer distinguish cats vs dogs. The end result of trusting technology we don’t fully understand. Q    Go slow and go small. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it. One notable … First, some definitions. I talked a bit about data bias above but there are plenty of other issues that can be introduced via data. You train it and train it and train it. Machine learning models are built by people. This is a silly one and might be hard to believe – but its a good example to use. Here’s an example that I ran across recently. People have biases whether they realize it or not. Malicious VPN Apps: How to Protect Your Data. This prevents complicated integrations, while focusing only on precise and concise data feeds. There may be some outliers (and I’d love to add those outliers to my list if you have some to share). S    is a technology consultant, investor and entrepreneur with an interest in using technology and data to solve real-world business problems. Machine Learning has a reputation for being one of the most complex areas of computer science, requiring advanced mathematics and engineering skills to understand it. One thing that can help is hiring an experienced machine learning team to help. How This Museum Keeps the Oldest Functioning Computer Running, 5 Easy Steps to Clean Your Virtual Desktop, Women in AI: Reinforcing Sexism and Stereotypes with Tech, From Space Missions to Pandemic Monitoring: Remote Healthcare Advances, The 6 Most Amazing AI Advances in Agriculture, Business Intelligence: How BI Can Improve Your Company's Processes. What happens is this – an investing strategy (e.g., model) is built using a particular set of data. If you use 100 data points, your contour is going to look all squiggly. Error diagnosis and correction. Not too long ago, it was considered state of the art research to make a computer distinguish cats vs dogs. However, there are times when using machine learning is just unnecessary, does not make sense, and other times when its implementation can get you into difficulties. His work has appeared in online magazines including Preservation Online, a project of the National Historic Trust, and many other venues. R    These days, computers are so smart, they can figure everything out for themselves. P    This near-immediate response is critical in a niche where bots, viruses, worms, hackers and other cyber threats can impact … There is no earthly limitations to the kind of blessings that comes in the form of machine learning. First, some definitions. Transport for New South Wales and Microsoft have partnered to develop a proof of concept that uses data and machine learning to flag potentially dangerous intersections and reduce … In the post, I don’t restrict the discussion to big data (but others do). The real problem,… Read more ». Image-scaling attacks vs other adversarial machine learning techniques In their paper, the researchers of TU Braunschweig emphasize that image scaling attacks are an especially serious threat to AI because most computer vision machine learning models use one of a … Machine learning has eliminated the gap between the time when a new threat is identified and the time when a response is issued. Limitation 1 — Ethics Machine learning, a subset of artificial intelligence, has revolutionalized the world as we know it in the past decade. What can you do as a CxO looking at machine learning / deep learning / AI to help mitigate these machine learning risks? Not anymore. The dangers of machine learning, AI can be mitigated through strong partnerships. You can't have bad data when your machine learning decisions affect real people. Take your time to understand the risks inherent in the process and find ways to mitigate the machine learning risks and challenges. Governments around the world are racing to pledge support to AI initiatives, but they tend to understate the complexity around deploying advanced machine learning systems in the real world. Makes sense, right? 5 Common Myths About Virtual Reality, Busted! Preface. Root out bias. While machine learning may not create sentient AI that try to take over the world, they are still dangerous. Think about this when trying to implement machine learning in an enterprise context. In addition, he is an entrepreneur that has launched a few companies with the most recent being a company focused on proving data analytics and visualization services to the financial markets. […] starting small allows you to better understand the risks involved (of which there are many). Managing bias is a very large aspect to managing machine learning risks. Thus, instead of manually analyzing data or inputs to develop computing models needed to operate an automated computer, software program, or processes, machine learning systems can automate this entire procedure simply by learning from experience. You can't have bad input when you're operating a self-driving vehicle. What happened? T    This can’t be further from the truth. Reinforcement Learning Vs. Preface. Make sure the data you are feeding your machine learning models are varied across both data types, timeframes, demo-graphical data-sets and as many other forms of variability that you can find. Killer robots stalk the ruined landscape. Why are some companies contemplating adding 'human feedback controls' to modern AI systems? For example, If you start with that big project and realize that most of […], […] starting small allows you to better understand the risks involved (of which there are many). Many people already participate in the field’s work without recognition or pay. It is based on the use of algorithms to give computers the ability to “learn” and make predictions on data. That can really mess up any business process. However, despite its numerous advantages, there are still risks and challenges. This is a nuisance when it comes to ironing out the kinds of decision support systems that machine learning provides, but it's much more serious when it's applied to any kind of mission-critical system. So really, the path toward successful machine learning is sometimes fraught with challenges. Now, I’m not a huge fan of the book (the book is a bit too politically bent and there are too many uses of the words ‘fair’ and ‘unfair’….who’s to judge what is fair?) How Machine Learning Can Improve Supply Chain Efficiency, How Machine Learning Is Impacting HR Analytics, Data Catalogs and the Maturation of the Machine Learning Market, Reinforcement Learning: Scaling Personalized Marketing. One of the things that naive people argue as a benefit for machine learning is that it will be an unbiased decision maker / helper / facilitator. Machine learning allows computers to take in large amounts of data, process it, and teach themselves new skills using that input. It may be true that big data holds some special thrall over us and gives us confidence in questionable findings–more confidence than we would have with smaller data sets. Regardless of what you call this risk…its a risk that exists and should be carefully managed throughout your machine learning modeling processes.