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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

Bloomberg

(Bloomberg) -- New Yorks apartment investors are suddenly waist-deep in distress.By December, they were behind on $395 million of debt backed by mortgage bonds, almost 150 times the level a year earlier, according to Trepp data on commercial mortgage-backed securities. Tenants in rent-stabilized units owe at least $1 billion in rent and wealthier ones are fleeing the city, leaving behind vacancies and pushing newly-built luxury towers into foreclosure.For years, as crime dwindled and rent climbed in New York, investors gobbled up apartment buildings. But with the citys economy and culture crushed by Covid-19, mounting job losses have derailed the gentrification boom and put financial pressure on landlords.The people who specialize in mortgage workouts are the busiest people in New York real estate, said Barry Hersh, a clinical associate professor of real estate at New York University.The developers who are in the most trouble pushed hard into Harlem and the Brooklyn hipster hubs of Crown Heights, Flatbush and Bushwick, squeezing out working-class residents by building new expensive units. Now, theyre grappling with eviction bans and new tenant protections as rent falls across New York.Colony 1209, a steel-gray apartment building, opened six years ago in the heart of Bushwick, an industrial vision of urban chic, with a billiards room and 24-hour doorman. The website pitched one bedrooms for $2,500 to like-minded settlers in the mostly Black and Hispanic neighborhood, which it called Brooklyns new frontier.Now Colony, renamed Dekalb 1209, faces foreclosure after owner Spruce Capital Partners defaulted on a $46 million mortgage. The five-year interest-only loan matured in October and was not extended, triggering the default, according to monthly filings by the loans servicer, Wells Fargo & Co.The lender is filing to repossess the building -- as soon as New Yorks foreclosure moratorium expires -- while simultaneously discussing workout alternatives with the borrower. Spruce could not be reached for comment.Right before Covid hit, investors were willing to pay top-dollar for luxury buildings like Colony. They wanted alternatives to rent-regulated buildings, which saw values crimped by a 2019 law that banned tactics landlords depended on to convert rent-stabilized units to market-rate.That was the bright spot until the pandemic happened, said Victor Sozio, executive vice president at Ariel Property Advisors, a commercial brokerage firm in New York City.Plans StymiedEmerald Equities, a fast-growing condo conversion specialist, filed for bankruptcy in December on buildings in Harlem. In its filing, the company said its well-laid plans were stymied by the tenant-friendly law. Residents organized a rent strike, then collections plunged even more after the pandemic, driving Emerald to hand ownership to LoanCore Capital, which loaned $203 million for the project.Doug Kellner, an attorney for Emerald tenants, blames the current market troubles on New Yorks eviction ban because it came without any accompanying financial support.Everybody realizes that rent is the green blood that keeps a building operational, Kellner said.Across the boroughs, rents are on a downward spiral, as landlords try to fill empty apartments with ever-sweeter tenant concessions -- only to see the number of vacant listings surge further.In Manhattan, available units nearly tripled in December from a year earlier, and the median rent plunged 17% to $2,800, according to data from Miller Samuel Inc. and Douglas Elliman Real Estate. Rents are down 11% in Brooklyn and 18% in Northwest Queens, where starry-eyed developers built glassy apartment fortresses along the waterfront for young midtown professionals.In some ways, investors may be better insulated than after the 2008 financial crisis. Lenders generally required bigger down payments and underwrote loans based on current rents rather than expectations for the future, said Shimon Shkury, Ariels president. If the vaccine works and college students and office workers start to return, so will the market, Shkury said.I dont think there will be as much distress as you think, he said.Deregulating RentsLenders have already put $1.4 billion of commercial-backed multifamily debt on watchlists because of issues such as rising vacancies or impending maturities. Thats 19% of all outstanding debt, compared with 22% at the nadir of the financial crisis.The trouble will filter from highly-leveraged investors who expanded quickly to lenders with the most aggressive underwriting, says NYUs Hersh.There will be banks that go under, he said.At the same time, the market for multifamily buildings has gone soft. The total dollar volume of New York City multifamily sales was $4.5 billion in 2020, a 61% plunge from 2018, before the pandemic or the new rent laws, according to a report by Ariel.Still, firms such Limekiln Real Estate Investment Management, see opportunities. The company made $224 million in New York multifamily loans in the second half of 2020, up from $9.3 million before the pandemic. Its easier to extract better terms in a lenders market, said Scott Waynebern, Limekilns president.Its tricky to find where the bottom is, he said.For more articles like this, please visit us at bloomberg.comSubscribe now to stay ahead with the most trusted business news source.2021 Bloomberg L.P.

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Unisys to Research Use of Artificial Intelligence and Machine Learning to Detect Deceitful and Persuasive Writing for Australia's Defence and National...

This article was originally published here

J Arthroplasty. 2020 Dec 30:S0883-5403(20)31300-0. doi: 10.1016/j.arth.2020.12.040. Online ahead of print.

ABSTRACT

BACKGROUND: As the prevalence of hip osteoarthritis increases, the number of total hip arthroplasty (THA) procedures performed is also projected to increase. Accurately risk-stratifying patients who undergo THA would be of great utility, given the significant cost and morbidity associated with developing perioperative complications. We aim to develop a novel machine learning (ML)-based ensemble algorithm for the prediction of major complications after THA, as well as compare its performance against standard benchmark ML methods.

METHODS: This is a retrospective cohort study of 89,986 adults who underwent primary THA at any California-licensed hospital between 2015 and 2017. The primary outcome was major complications (eg infection, venous thromboembolism, cardiac complication, pulmonary complication). We developed a model predicting complication risk using AutoPrognosis, an automated ML framework that configures the optimally performing ensemble of ML-based prognostic models. We compared our model with logistic regression and standard benchmark ML models, assessing discrimination and calibration.

RESULTS: There were 545 patients who had major complications (0.61%). Our novel algorithm was well-calibrated and improved risk prediction compared to logistic regression, as well as outperformed the other four standard benchmark ML algorithms. The variables most important for AutoPrognosis (eg malnutrition, dementia, cancer) differ from those that are most important for logistic regression (eg chronic atherosclerosis, renal failure, chronic obstructive pulmonary disease).

CONCLUSION: We report a novel ensemble ML algorithm for the prediction of major complications after THA. It demonstrates superior risk prediction compared to logistic regression and other standard ML benchmark algorithms. By providing accurate prognostic information, this algorithm may facilitate more informed preoperative shared decision-making.

PMID:33478891 | DOI:10.1016/j.arth.2020.12.040

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Development of a Novel, Potentially Universal Machine Learning Algorithm for Prediction of Complications After Total Hip Arthroplasty - DocWire News