Mediaboss Marketing

Originally published in UX Collective, Jan 19, 2021.

For an industry that prides itself on moving fast, the tech community has been remarkably slow to adapt to the differences of designing with AI. Machine learning is an intrinsically fuzzy science, yet when it inevitably returns unpredictable results, we tend to react like its a puzzle to be solved; believing that with enough algorithmic brilliance, we can eventually fit all the pieces into place and render something approaching objective truth. But objectivity and truth are often far afield from the true promise of AI, as well soon discuss.

I think a lot of the confusion stems from language;in particular the way we talk about machine-like efficiency. Machines are expected to make precise measurements about whatever theyre pointed at; to produce data.

But machinelearningdoesnt produce data. Machine learning producespredictionsabout how observations in the present overlap with patterns from the past. In this way, its literally aninversionof the classicif-this-then-thatlogic thats driven conventional software development for so long. My colleague Rick Barraza has a great way of describing the distinction:

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When Are We Going to Start Designing AI With Purpose? Machine Learning Times - The Predictive Analytics Times

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Read next: Forget Hyperloop, check out Chinas new 620kmph maglev prototype

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While COVID-19 circumstances have forced organizations to meet remotely on the Zoom application, it has enabled groups like the Rotary Club of New Canaan to invite speakers from far away.

The clubs Zoom Christmas party included a previous Rotary International Scholar, Yuri Nakashima, from her home in Japan. This past weeks luncheon speaker was New Canaan native John Gnuse, son of Rotarian Jeanne Gnuse, and her late husband, Tom. Gnuse spoke to the club from San Francisco, where he is managing director at Lazard, on the topic of The Machine Learning Revolution.

Happily, the Zoom format enabled his sister, Dr. Karen Gnuse Nead, in Rochester, N.Y., and uncle, William Pflaum, in Menlo Park, Calif., to attend as well.

Gnuses career has focused on mergers and acquisitions of major technology companies, e.g. Google, IBM, Microsoft, Amazon and Apple, etc., and as such, he is a great guide to the world of machine learning.

His talk highlighted the progress which advanced computing power, and capacity have made possible.

Machine learning refers to the ability for complex algorithms to improve accuracy, and performance based on continuous experience with additional training data.

With these capabilities, complex, iterative processes using with multiple parameters have yielded sophisticated neural networks that can learn.

This has yielded sophisticated tools, and solutions that were not previously possible, but which we rely on now for so much of daily life such as for web search, speech recognition, (Alexa, Siri), medical research and financial optimization models, etc., to name a few.

In answer to concerns about where advances in artificial intelligence will take us, John referred to the guardrails already in place, and those which continue to be applied as key elements of the machine learning revolution. The field raises significant legal, ethical and morality challenges, which will continue to be evaluated as do concerns regarding bias, and fairness as the results of these networks impact people everywhere.

For more on the club, contact Alex Grantcharov, president, at alex.grantcharov@edwardjones.com, follow the club at http://www.facebook.com/NewCanaanRotary, newcanaanrotary on Instagram or at the clubs website, newcanaanrotary.org

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New Canaan native speaks on Machine Learning Revolution - New Canaan Advertiser

Machine learning is essentially making a PC to play out a task without expressly programming it. In this day and age, each framework that does well has a machine learning algorithm at its heart. Machine learning is at present probably the most sizzling topics in the business and organizations have been racing to have it consolidated into their products, particularly applications

As indicated by Forbes, Machine learning patents developed at a 34% rate somewhere between 2013 and 2017 and this is simply set to increment later on. Furthermore, Python is the essential programming language utilized for a significant part of the innovative work in Machine Learning. To such an extent that Python is the top programming language for Machine Learning as indicated by Github

Machine learning isnt just utilized in the IT business. Machine learning likewise plays an important role in advertising, banking, transport, and numerous different businesses. This innovation is continually advancing, and subsequently, it is methodically acquiring new fields in which it is an integral part.

Python is a high-level programming language for overall programming. Besides being an open-source programming language, python is an extraordinarily interpreted, object-oriented, and interactive programming language. Python joins surprising power with clear syntax. It has modules, classes, special cases, significant level dynamic data types, and dynamic composing. There are interfaces to numerous system calls and libraries, as well as to different windowing frameworks.

Easy and Fast Data Validation

The job of machine learning is to identify patterns in data. An ML engineer is answerable for harnessing, refining, processing, cleaning, sorting out, and deriving insights from data to create clever algorithms. Python is easy while the topics of linear algebra or calculus can be so perplexing, they require the maximum amount of effort. Python can be executed rapidly which allows ML engineers to approve an idea immediately.

Different Libraries and Frameworks

Python is already very well-known and thus, it has many various libraries and frameworks that can be utilized by engineers. These libraries and frameworks are truly valuable in saving time which makes Python significantly more well-known.

Code Readability

Since machine learning includes an authentic knot of math, now and then very troublesome and unobvious, the readability of the code (also outside libraries) is significant if we need to succeed. Developers should think not about how to write, but rather what to write, all things considered.

Python developers are excited about making code that is not difficult to read. Moreover, this specific language is extremely strict about appropriate spaces. Another of Pythons advantages is its multi-paradigm nature, which again empowers engineers to be more adaptable and approach issues utilizing the simplest way possible.

Low-entry Barrier

There is an overall shortage of software engineers. Python is not difficult to get familiar with a language. Hence, the entry barrier. is low. Whats the significance here? That more data scientists can become experts rapidly and thus, they can engage in ML projects. Python is fundamentally the same as the English language, which makes learning it simpler. Because of its easy phrase structure, you can unhesitatingly work with complex systems.

Portable and Extensible

This is a significant reason why Python is so mainstream in Machine Learning. So many cross-language tasks can be performed effectively on Python due to its portable and extensible nature. There are numerous data scientists who favor utilizing Graphics Processing Units (GPUs) for training their ML models on their own machines and the versatile idea of Python is appropriate for this.

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Latest News Why Should Python Be Used in Machine Learning? - Analytics Insight

Organizations havent been able to capitalize on the exponential growth in unstructured data in recent years despite the availability of sophisticated machine learning tools, according to the latest research from Info-Tech Research Group.

When it comes to the strategic use of machine learning, a quarter of respondents in Info-Techs latest tech trends report claim they wont be mature enough for at least another four years. Thirty-one per cent expect at least another year before they can hit the ground running. Just under 15 per cent claim theyre mature enough today to use machine learning to actually augment business. Out of the more than 200 global survey respondents, most of whom work in IT as a manager or director, 59 of them were from Canada.

It was a bit surprising to hear that technology which has been available for many years is still failing to be turned into a transformational force within organizations, according to Brian Jackson, Info-Techs research director for CIO, strategy, and digital transformation.

The technology is very available now, Jackson said in an interview. And we are seeing some organizations use it to build chatbots and other tools to automate customer service.

But Jackson says its a bit alarming to see such a lack of innovation around the use of machine learning outside of the startup scene.

Its like when people in the year 2000 thinking oh, the internet. I dont think thats going to be a big deal, he explained.

It reflectsa lack of maturity plaguing most IT departments. Only six per cent of survey respondents felt their IT departments maturity level had the capacity to drive change across the business. Even with more than 70 per cent of survey respondents noting AI and machine learning will be very important over the next five years, only 14 per cent felt their IT was ready to expand the business. Most organizations feel that IT is optimizing the business, while 34 per cent view IT as a support mechanism.

The streetwear collection business is tough. Getting your own collection off the ground, combined with having to source out designers, pattern cutters and merchandisers youre probably looking down the barrel of a six to eight-month process. Toronto startup Urbancoolab is an AI-powered fashion design platform designed to reduce the headaches associated with that process.

Info-Tech cited the startup in its research as a prime example of AI and machine learning running at the top of the value stream. The research firm went as far as to say the startup is reinventing a business category. Its tough to argue with the results.

Since 2020, Urbancoolab has worked with 30 celebrity artists to launch commercial designs. The research paper highlights how the startup can take a new design to market on its e-commerce site within 24 hours. Urbancoolab can find patterns in unstructured data in ways that humans cant, providing new designs rapidly. It can also be used to help confirm which designs will find the most market success. This lightning-fast turnaround is a big deal, but larger businesses playing in the same arena are simply not as nimble.

Many large companies lag behind disruptive first movers because they adhere to legacy processes and technology stacks, Info-Tech noted. That organizational structure was created long before AIs emergence, so applying AI in a meaningful way is difficult. Theres also a scarcity of true AI talent available on the market.

The untapped potential of AI and machine learning is obvious, but so are some of the uncertainties. Machine learning algorithms are only as good as the data used to train them. If the algorithms running underneath your datasets hood are limited or flawed, thats bad news for the company.

Most companies are in no position to hire a skilled AI scientist, making talent really hard to come by. Combine that with the ongoing privacy concerns related to machine learning algorithms touching customer or employee data, and businesses are faced with several uncertainties when asking IT to go beyond supporting the business.

Jackon says channel partners have an obvious opening to address these gaps.

Modern channel providers should look at themselves as the central service that your customers can rely upon to transform their business, he explained. We need companies that are able to look outside of themselves and look at opportunities to inject innovative new ideas by working with other companies in the same industry.

Info-Tech hosted a webinar recently going over some of the data from its trends report. An on-demand link can be found here.

Jim Love, Chief Content Officer, IT World Canada

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Research says organizations still struggle to cash-in on machine learning - IT World Canada