Posts tagged Jim Sinur
Top 5 Facets of AI

AI will be the gem of digital going forward for a long time. It is a co-driver of smarts in both automation and customer excellence efforts along with static algorithms. AI can learn, handle fuzzy problems, and help with increasing the probability of success in decisions, assist humans in interacting with traditional rule-based organizational systems and reaching shifting goals. There are five facets of AI that are shining bright now and for the future. There could be more down the road as AI progresses over time, but these are the top five right now.

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Machine Learning for Shovel Tooth Failure Detection

The steel teeth on mining excavation equipment like rope shovels and front end loaders are wear items that must be replaced as part of regular maintenance. During normal operation, the connection that affixes a tooth to the shovel or loader bucket occasionally fails, causing tooth detachment. A detached tooth presents a serious hazard if it enters the haulage cycle and makes its way into a crushing unit, where it may become stuck and require the dangerous task of manual removal. Furthermore, wayward teeth cause substantial lost time and production due to jammed crushers and damage to downstream processing equipment. Therefore, it is critical to detect when a shovel tooth goes missing as soon as possible so that preventative action may be taken.

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Failing Architecture? Digital Twins to the Rescue!

We are hearing the tales of severely ill or dying enterprise architectural efforts in even the best of organizations. It really shouldn’t be a surprise as architectural efforts are long, slow and change before they are complete. Even if an organization manages to complete one, things are changing so fast the value of even a rare complete architecture is dubious at best. Leveraging digital twins can be a game changer for architecture. A digital twin is a digital replica of physical assets, processes, people, places, systems and devices that can be used for management purposes.

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Is AI Making Us All Data Hoarders?

Today the emphasis on AI is nearly all about machine learning and a form of machine learning called deep learning. These are data heavy (Data, Events, Voice & Image) approaches that deliver some very nice benefits, but are we getting hooked on data? We see folks everywhere creating data lakes about to be data oceans that we are going to boil later. Meanwhile we have to pay homage to expensive Data Czars and Data Scientists because we want to keep more data for the future and somehow AI will make sense of it later. I’m not so sure this is a strategy that will lead us to be competitive with others in the world.

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The Unexpected Consequences of Big Data

Big Data is the unexpected resource bonanza of the current century. Moore’s Law driven advances in computing power, the rise of cheap storage and advances in algorithm design have enabled the capture, storage, and processing of many types of data previously that were unavailable for use in computing systems. Documents, email, text messages, audio files, and images are now able to transform into a usable digital format for use by analysis systems, especially artificial intelligence. The AI systems can scan massive amounts of data and find both patterns and anomalies that were previously unthinkable and do so in a timeframe that was unimaginable. While most of the uses of Big Data have been coupled with AI/machine learning algorithms so companies and understand their customer's choices and improve their overall experience (think about recommendation engines, chatbots, navigation apps and digital assistants among others) there are uses that are truly industry transforming.

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How to Survive The Upcoming AI Tsunami

Like it or not we are all going to get inundated by AI, Robotics and Automation opportunities. As organizations or individuals, we will have to decide how we are going to respond. Since we can no longer count on AI going back into another “AI Winter” because it is not going away. We will have to decide on AI as a trend and each AI encounter we will be facing. Our choices on AI will either make us win or lose in the short term and the long run. What are the responses to AI we can exercise?

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How Will AI Help Me?

We hear a lot about how AI is progressing well these days and how it will help companies with all kinds of efforts from assisting customer pleasing experiences to implementing massive automation efforts. Well, it looks like AI will make it this time and there is a low probability that there will be another AI winter coming. So now we all have to deal with living with AI. I have been contemplating working with AI in my life, so I question how will AI help me and how do I work with it? Will it be like other technology that I have to learn to for life success or will it be more like a person that I have to get along with to thrive in the future? Will AI make our lives better or should we be practicing for those apocalypse scenarios I keep reading about? I think the answer will vary by roles the life that we play and where AI will add value to our lives. I think AI is evolving but in a good way.

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Practicing Safe Coboting

Like it or not you will be interacting with either physical or software robots soon. A cobot or co-robot (from collaborative robot) is a robot intended to physically interact with humans in a shared workspace. It’s not just the obvious robots to consider. Additionally, there are software bots that can either assist you with knowledge/data mining or actions that eliminate drone work. While there will be initial interfacing issues and learning curves to get by, there are a number of long term issues to consider when interacting with bots.

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So How Goes That AI Spring?

While AI hasn’t reached its full potential or its eventual impact yet, AI is making good progress in many directions simultaneously. Let’s examine some of the progress to date. While I’m sure that AI is adding value, I’m also sure there is more progress that is not visible yet as it is in the labs or being pioneered in several scientific avenues. Let’s look at the value add of AI to date.

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Differentiating with Algorithms: A Case Study

One can't go anywhere today without hearing about AI, but I would say that half of it is around projecting how AI will be affecting our future and the other half is about data focused approaches. While there is nothing wrong about these discussions and articles, most of us are more interested in the results of AI which represents a family of approaches and algorithms in action to apply to business and everyday problems. Even though there are three major approaches to AI

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Turbo Charge AI with Right Brain-Based Reasoning

AI is blossoming, but the real advantage of AI is yet to come. While organizations are mesmerized by the power of AI on left-brained activities of facts, rules, and logic, the big opportunity lies fallow. It's the right-brained activities that help with insight, interpretation, intuition, judgment, and reasoning where the big benefits can be gleaned. The leverage of general policies, creativity, and constraints is not natural in the world of AI today. Because it is difficult today, many pass reasoning and judgment by these days. What if there was a way to deliver right-brained behavior with left-brained activities? There is, but I'll get to that later.

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AI-Assisted Digital Assistants Really Work

Black & Veatch, an employee-owned, global leader in building critical human infrastructure in energy, water, telecommunications, and government services, is on a journey to leverage AI-assisted bots, called virtual experts, to better capture and interact with engineering knowledge and standards. The goal of this emerging effort is to experiment with ways to better capture knowledge and expertise within the company. Ultimately, this initiative would lead to a reduced amount of time required to locate desired information and create an opportunity for continued innovation to better support the future.

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AI & Big Data; Better Together

Big data, unstructured or structured, fast or slow, in multiple contexts or one is a beast to manage. Big data is growing fast fueled by the democratization of data and the IoT environment. Often organizations simply control what they know they get results from and then store the rest for future leverage. In fact, most organizations use less than 20% of their data, leaving the remaining 80%, and the insights it contains, to be left outside to the operational and decision-making processes. Imagine if you used only 20% of any service you paid for every month and ignored the other 80%! This is exactly what we are doing with data. Fortunately, there is hope as this is where Big Data can start to rely on AI and engage in a “cycle of leverage”. Presently, the interaction between AI and Big Data is in the early stages, and organizations are discovering helpful methods, techniques, and technologies to achieve meaningful results. Typically these efforts are neither architected nor managed holistically. Our work has shown there is an emerging “Cycle of Big Data” that we and would like to describe and share with you where we see AI can help.

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Digital Models from Scans Assisting with Industrial Security

If you have not heard of active shooter incidents recently, you have been living under a rock. Imagine what 2D floor plans and 3D models can do to assist these situations. Even without intelligent digital assistants, law enforcement can get a better handle on the structure involved with any particular incident. While these digital models are helpful, they are difficult and time consuming to create. This case study is about the evolving scanning, creation and leverage of visual digital models on a large scale.

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Is AI Data Driven, Algorithm Driven, or Process Driven?

There are those that think that data is the oil of AI and the focus should be clean data, data science and deep understanding of what the data means. There are those that say data is meaningless without context that can be with other data, models/algorithms or processes. Let’s explore the arguments in a concise fashion to discover the advantage of each view.

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