Machine Learning (ML)is constantly being adopted by diverse organizations in an enthusiasm to acquire answers and analysis. As the embracing highly increases, it is often forgotten that machine learning has its flaws that need to be addressed for acquiring a perfect solution.

Applications of artificial intelligence andmachine learning are using new toolsto find practical answers to difficult problems. Companies move forward with the emerging technologies to get a competitive edge on their working style and system. Through the process, organizations are learning a very important lesson that one strategy doesnt fit for all.Business organizations want machine learningto do analysis on large data, which is complex and difficult. They neglect the fact that machine learning cant perform on diverse data storage and even if it does, it will conclude with a wrong prediction.

Analysing unstructured and overwhelming large datasets on machine learning is dangerous. Machine learning might conclude with a wrong solution while performing predictive analysis on such data. The implementation of the misconception in a companys working system might drag down its improvement. Many products that incorporatemachine learning capabilitiesuse predetermined algorithms and many diverse ways to handle data. However, each organizations data has different technical characteristics that might not go well with the existing machine learning configuration.

To address the problems where machine learning falls short, AutoML takes head-on in the companys data analysis perspective. AutoML takes over labour intensive job of choosing and tuning machine learning models. The new technology takes on many repetitive tasks where skilful problem definition and data preparation are needed. It reduces the need to understand algorithm parameters and shortening the compute time needed to produce better models.

Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The technology focuses on the development of computer programs that can access data and use it for themselves. It is a model created and trained on a set of previously gathered data, often known as outcomes. The model can be used tomake predictions using that data.

However, machine learning cant get accurate results all the time. It depends on the data scientist handling the machine learning configurations and data inputs. A data scientist studies the input data and understands the desired output to solve business problems. They choose the apt mathematical algorithm from a dozen and tune those parameters called hyperparameters and evaluate the resulting models. The data scientist has the responsibility to adjust the algorithms tuning parameters again and again until the machine learning model produces the desired result. If the results are not tactic, then the data scientist might even start from the very beginning.

Machine learning system struggles to function when the data is too large or unorganised. Some of the other machine learning issues are,

Classification- The process of labeling data can be thought to as a discrimination problem, modeling the similarities between groups.

Regression- Machine learning staggers to predict the value of a new unpredicted data.

Clustering- Data can be divided into groups based on similarity and other measures of natural structure in data. But, human hands are needed to assign names to the groups.

As mentioned earlier, machine learning alone cant address the datasets of an organisation to find predictions. Here are some reasons why tuning a machine learning algorithm is challenging to choose and how AutoML can prove to be useful at such instances.

Choosing the right algorithm: It is not always obvious to choose a perfect algorithm that might work well for building real-value predictions, anomaly detection and classification models for a particular data set. Data scientists have to go through many well-known algorithms of machine learning that could suit the real-world situation. It could take weeks or even months to come up with the right algorithm.

Selecting relevant information: Data storage has diverse data variables or predictors. Henceforth, it is hard to tell which of those data points are significant for making a decision. This process of selecting relevant information to include in data models is called feature selection.

Training machine learning models: The most difficult process in machine learning is to choose a subset of data that can be used for training a machine learning model. In some cases, training against some data variables or predictors can increase training time while actually reducing the accuracy of the ML model.

Automated machine learning (AutoML)basically involves automating the end-to-end process of applying machine learning to real-world problems that are actually relevant in the industry.AutoML makes well-educated guessesto select a suitable ML algorithm and effective initial hyperparameters. The technology tests the accuracy of training the chosen algorithms with those parameters and makes tiny adjustments, and tests the results again. AutoML also automates the creation of small, accurate subsets of data to use for those iterative refinements, yielding excellent results in a fraction of the time.

In a nutshell, AutoML acts as a right tool that quickly chooses, builds and deploys machine learning models that deliver accurate results.

The rest is here:

AutoML Alleviates the Process of Machine Learning Analysis - Analytics Insight

New AI capabilities pave way for an ultra-personalized customer experience

PISCATAWAY, N.J., Oct. 14, 2020 /PRNewswire/ --Futurism Technologies, a leading provider of digital transformation solutions, is bringing to life its Futurism Dimensions business commerce suite with additional artificial intelligence and machine learning capabilities. New AI capabilities will help online companies provide an exceptional personalized online customer experience and user journeys. Futurism Dimensions will not only help companies put their businesses online, but would also help to completely digitize their commerce lifecycle. The commerce life cycle includes digital product catalog creation and placement, AI-driven digital marketing, order generation to fulfillment, tracking, shipments, taxes and financial reporting, all from a unified platform.

With the "new norm," companies are racing to provide a better online experience for their customers. It's not just about putting up a website today, it's about creating personalized and smarter customer experiences. Using customer behavioral analysis, AI, machine learning and bots, Futurism's Dimensions creates that personalized experience. In addition, with Futurism Dimensions, companies become more efficient by transforming the entire commerce value chain and back office to digital.

"Companies such as Amazon have redefined online customer experience and set the bar very high. Every company will be expected to offer personalized, easy-to-use, online experience available from anywhere at any time and on any device," said Sheetal Pansare, CEO of Futurism Technologies. "We've armed Dimensions with advanced AI and ML to help companies provide exceptional personalized experiences to their customers. At the same time, with Dimensions, they can digitize their entire commerce value chain and become more efficient with business automation. Our ecommerce platform is affordable and suited for companies of all sizes," added Mr. Pansare.

Story continues

Futurism Dimensions highlights:

Secure and stable platform with 24/7 support and migration

As cybercrimes continue to evolve, e-commerce companies ought to keep up with advanced cybersecurity developments. Futurism Dimensions prides itself on its security for customers allowing them to receive the latest in technological advancements in cybersecurity. Dimensions leverages highly secure two-factor authentication and encryption to safeguard your customers' data and business from potential hackers.

To ensure seamless migration from existing implementations, Dimensions integrates with most legacy systems.

Dimensions offers 24/7 customer support, something you won't find with some of the dead-end platforms of the past. Others will simply have a help page or community forum, but that doesn't necessarily solve the problem. It can also be costly if you need to reach someone for support on other platforms, whereas Dimensions support is included in your plan.

Migrating to Dimensions is a seamless transition with little to no downtime. Protecting online businesses from cyber threats is a top priority while transitioning their websites from another platform or service. You get a dedicated team at your disposal throughout the transition to ensure timely completion and implementation.

Heat Map, Customer Session Playback, Live Chat and Analytics

Dimensions offers intelligent customer insights with Heat Map tracking, Full customer session playback, and live chat allowing you to understand customers' needs. Heat Map will help you identify the most used areas of your website and what your customers are clicking on. Further, customer session playback will help you identify how customers arrived at certain products or pages. Dimensions also has a live customer session that helps you provide prompt support.

Customer insights and analytics are lifeblood for any e-business in today's digital era. Dimensions offers intelligent insights into demographics to help you market to your target audiences.

Highly personalized user experience using Artificial Intelligence

Dimensions lets you deploy smart AI-powered bots that use machine learning algorithms to come up with smarter replies to customer questions thus, reducing response time significantly. Chatbots can help address customer queries that usually drop in after business hours with automated and pre-defined responses. Eureka! Never lose a sale.

Business Efficiency and Automation using AI and Machine Learning

AI and machine learning can help predict inventory and automate processes such as support, payments, and procurement. It can also expand business intelligence to help create targeted marketing plans. Lastly, it can give you live GPS logistics tracking.

Mobile Application

Dimensions team will design your mobile site application to look and function as if a consumer were viewing it on their computer. Fully optimized and designed for ease of use while not limiting anything from your main site.

About Futurism Technologies

Advancements in digital information technology continue to offer companies with the opportunities to drive efficiency, revenue, better understand and engage customers, and redefine their business models. At Futurism, we partner with our clients to leverage the power of digital technology. Digital evolution or a digital revolution, Futurism helps to guide companies on their DX journey.

Whether it is taking a business to the cloud to improve efficiency and business continuity, building a next-generation ecommerce marketplace and mobile app for a retailer, helping to define and implement a new business model for a smart factory, or providing end-to-end cybersecurity services, Futurism brings in the global consulting and implementation expertise it takes to monetize the digital journey.

Futurism provides DX services across the entire value chain including e-commerce, digital infrastructure, business processes, digital customer engagement, and cybersecurity.

Learn more about Futurism Technologies, Inc. at http://www.futurismtechnologies.com

Contact:

Leo J Cole Chief Marketing Officer Mobile: +1-512-300-9744 Email: communication@futurismtechnologies.com

Website: http://www.futurismtechnologies.com

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Futurism Reinforces Its Next-Gen Business Commerce Platform With Advanced Machine Learning and Artificial Intelligence Capabilities - Yahoo Finance

October 13, 2020 12:30 ET | Source: Purebase Corporation

IONE, CA, Oct. 13, 2020 (GLOBE NEWSWIRE) -- Purebase Corporation (OTCQB: PUBC), a diversified resource company, headquartered in Ione, California, today announces that Dr. Newell Washburn, PhD, whom is an expert on machine learning and cheminformatics applied to complex materials applications has agreed to join the Purebase Advisory Board.

Dr. Washburn joins Dr. Karen Scrivener, PhD, Dr. Kimberly Kurtis, PhD, and Mr. Joe Thomas as part of the Purebase Advisory Board team that will provide expert guidance in the development and execution of Purebases rollout of next-generation, carbon emission reducing, supplementary cementitious materials (SCMs).

Purebases Chairman and CEO, Scott Dockter stated, We look forward to Dr. Washburn joining our team. He will be an asset and great resource as his primary focus is the use of data-driven approaches to formulate cementitious binders with high SCM content and to design chemical admixture systems for the broad deployment. In addition, his partnering with a broad range of chemical admixture and cement companies and the ARPA-E program in the Department of Energy. We are looking forward to working with him.

Newell R. Washburn, PhD is Associate Professor of Chemistry and Engineering at Carnegie Mellon University and CEO of Ansatz AI. Professor Washburn co-founded Ansatz AI to commercialize the hierarchical machine learning algorithm he and his collaborators developed at CMU for modeling and optimizing complex material systems based on sparse datasets. The company is currently working with clients in the US, Europe, and Japan on using chemical and materials informatics in product development and manufacturing. Professor Washburn received a BS in Chemistry from the University of Illinois at Urbana-Champaign, performed doctoral research at the University of California (Berkeley) on the solid state chemistry of magnetic metal oxides, and then did post-doctoral research in chemical engineering at the University of Minnesota (Twin Cities).

About Purebase Corporation

Purebase Corporation (OTCQB: PUBC) is a diversified resource company that acquires, develops, and markets minerals for use in the agriculture, construction, and other specialty industries.

Contacts

Emily Tirapelle | Purebase Corporation

emily.tirapelle@purebase.com,and please visit our corporate website http://www.purebase.com

Safe Harbor

This press release contains statements, which may constitute forward-looking statements within the meaning of the Securities Act of 1933 and the Securities Exchange Act of 1934, as amended by the Private Securities Litigation Reform Act of 1995. Those statements include statements regarding the intent, belief, or current expectations of Purebase Corporation and members of its management team as well as the assumptions on which such statements are based. Such forward-looking statements are not guarantees of future performance and involve risks and uncertainties, and that actual results may differ materially from those contemplated by such forward-looking statements. Important factors currently known to management that may cause actual results to differ from those anticipated are discussed throughout the Companys reports filed with Securities and Exchange Commission which are available at http://www.sec.gov as well as the Companys web site at http://www.purebase.com. The Company undertakes no obligation to update or revise forward-looking statements to reflect changed assumptions, the occurrence of unanticipated events or changes to future operating results.

Excerpt from:

Purebase Enhances Its Board of Advisors with An Expert on Machine Learning and Cheminformatics - GlobeNewswire

The worldwide spread of the COVID-19 pandemic has disrupted how people buy products and services and how they perceive e-commerce. The standardized lockdown rules across India and the growing hesitation among consumers to go outside and shop for essential goods have tilted the nation towards e-commerce.

Consumers have switched from shops, supermarkets, and shopping malls to online portals for the purchase of products, ranging from basic commodities to branded goods. Since the norm of social distancing has been initiated for almost the entirety of 2020, the scope of online purchases and online businesses is expected to surge. Many people are embracing the concept of online retail and there's been a visible surge in first time users on e-commerce portals. But this boom for e-commerce portals have only meant an increase in pressure on the logistics industry - an industry that employs over 8 million people in the country.

Well, to understand the bearing the pandemic's had on the logistics industry and how's technology played a key role in tackling some of the problems at hand, we spoke to Kapil Bharati, who's the Chief Technology Officer and Co-founder at Delhivery - one of India's biggest B2B & C2C logistic courier service providers.

An IIT Delhi alumnus and a mechanical engineer by qualification, Bharati has been working on the design, development of complex large-scale applications at Delhivery and specifically leads the Technology and Data Science divisions, providing overall technical direction to the organisation.

Here are some of the insights that he had to share with us.

Question: When you think of supply chain services and logistics from a lay-mans point-of-view, the use of technology definitely isn't the first thing that comes to mind. Being one of its co-founders, could you tell us a bit about Delhivery's journey in India over the past 9 years, while helping us understand how key a role technology plays in it?

Kapil Bharati: Since its inception in 2011, Delhivery has become India's largest supply chain services company. Today, with our nationwide network extending beyond 17,500 pin codes and 2,300 cities, we provide a full suite of logistics services such as express parcel transportation, LTL and FTL freight, reverse logistics, cross-border, B2B & B2C warehousing, and technology services.

We aim to build the operating system for commerce and utilise our scale and learning from the Indian market globally.

Our strategy is inherently composed of two interlocking flywheels - logistics and technology. We continue to aggressively invest in building world-class logistics infrastructure and couple it with cutting edge technology, design, and engineering capabilities.

The flywheels represent a fundamental pillar of our philosophy - deliver at scale to reduce costs; generate and process data at scale to bring further cost optimisation and service level improvements in our operations. These have allowed us to deliver speed, reliability, and cost-efficiency to over 10000 customers across our network that reaches over a billion consumers. At the heart of this transformation is the ability to capture, store, and process huge amounts of data.

SEE ALSO: These Tech Trends Will Impact Our Lives In A Post COVID-19 World

As we deliver over 1 million shipments every day, we undergo 30 million changes in the state of shipments in our network and generate over 100 million events from over 40k devices in the network. Leveraging machine learning and artificial intelligence techniques, each event makes our system more efficient - optimising facility locations, transportation routes, and movement of goods, predicting future events, creating dynamic last-mile delivery routes, and ensuring consistently high service levels. This ability acts as a significant differentiator and has powered our growth over the last 9 years.

Our vision is to create a global technology and data platform to provide real-time insights to businesses and create optimisation opportunities and real-time decision support for logistics and supply chain players worldwide.

Question: A large chunk of the country's services has been almost forced to adopt a tech-first approach ever since the COVID-19 pandemic became a part of our lives. How has Delhivery embraced this change and do you think the pandemic has thus served as a blessing in disguise?

Kapil Bharati: The fast spread of COVID-19 and multiple nationwide lockdowns posed serious challenges and uncertainties in our supply chain network.

Our technology stack acted as a significant differentiator during this time, enabling us to answer the holy trinity of what (essential/non-essential goods) could move where (containment zones; red/ orange/ green zone) and how (active lanes/ facilities).

Within the first 48 hours of the lockdown, we repurposed our address disambiguation and product categorization systems to identify customers living in the containment zones and essential shipments. Control systems were put in place to provide visibility and direction to our ground operators. These were crucial ingredients for businesses to reboot, as we advised our clients on the products, they could ship without the fear of getting stuck. We were operational in 4500 pin codes within 48 hours of the lockdown and over 15500 pin codes within a week.

Safety of our customers and employees is a top priority for us. Our teams are following stringent traceability protocols, undergoing daily health checks, and are stocked adequately with face masks, hand gloves and sanitizers. We are doing our best to deliver your packages safely. pic.twitter.com/xfZdWnjKKj

The closure of physical retail forced brands, distributors, and retailers to relook at their business models and directly connect with customers. We launched new services to quickly onboard such businesses in the healthcare, pharma, and food domains and set them up to adopt digital technologies for hyperlocal and contactless deliveries.

Internally, one of the major changes has been the organisation's split into an arm that works relentlessly on the ground and another that works from home to power technology, products, BD, and client experience. In the crucible of the pandemic lockdown, our teams showed remarkable resilience in adapting to both environments, expanding for us the possibility of attracting talent irrespective of where they are based.

Question: How big a role do you think machine learning and artificial intelligence play in being components of change in the landscape of the logistics industry as a whole?

Kapil Bharati: AI/ML starts to play an increasingly important role as we scale our operations to deliver millions of shipments a day. It becomes very hard at this scale, if not impossible, for ground operators to make optimal decisions on how shipments must be routed through the network.

AI/ML models allow us to automate this decision-making and push the boundaries of speed and efficiency as systems become capable of simultaneously evaluating thousands of variables that affect a shipments life cycle. As we ingest the huge amounts of data generated by our operations, we are constantly building the intelligence that powers these decision-making abilities.

Meet Mushtaque, our Last-Mile Agent, who is a resident of Mahim, Mumbai and has been associated with us for the last three years. If you wish to partner with us, please login at https://t.co/IGWIYZfqGr for more details. #PartnerWithDelhivery #PartnerTestimonials #PartnerProgram pic.twitter.com/N2VJYX8Kkh

Lets consider a simple example - the resolution of user addresses. In a country like India, one of the fastest-growing consumer markets, the address system is not highly structured. Most companies will lose visibility of an address beyond the pin code (median area of 80 sq km). Our proprietary address resolution engine, Addfix, has been trained over last-mile GPS traces across 750 million successful deliveries to help us accurately predict address locations to within 200m.

Similarly, our other AI/ML systems can predict preferred timeslots for attempting a shipment, predict whether a shipment can be flown or not based on its description, learn on-ground movement constraints based on recent location data, amongst others. These insights feed into our optimisation models, enabling us to ensure our shipments move at the fastest speed at minimum cost while respecting operational constraints and customer preferences.

Altogether, these systems work in tandem to minimise dependency on human decision making to give us a competitive advantage when it comes to both speed and efficiency. Every player in the logistics industry will need to adopt these tools across the board to remain competitive in the days to come.

Question: The global supply chain is already undergoing a major transformation enabled by Big Data and powered by data science teams using advanced technologies like artificial intelligence, blockchain, and robotics. We obviously hear businesses summarize this into fancy terms like 'Industry 4.0' and 'Supply Chain 4.0' but do you see supply chain digitization being a big differentiator in ensuring better efficiency?

Kapil Bharati: The fundamental change over the last few years has been the omnipresence of devices that generate rich data, the rise of the gig/ sharing economy, and the ability to process vast quantities of data in the background to create systemic intelligence or near real-time in the foreground to provide optimal direction.

This paves the way for a transformation from a highly controlled environment with rigid operating procedures, intensive training requirements, and a lack of adaptability to on-ground situations into an environment that continuously builds intelligence and can flexibly and dynamically direct variable supply through mobile/ internet devices to achieve objectives optimally. This kind of environment will not only be more flexible and adaptive but also capable of unlocking never seen before efficiencies and levels of service.

A key feature of this digital transformation will be the ease of collaboration and the participation of multiple types of actors - whether it is a large FMCG enterprise or a trucker with two trucks, or a college student with 4 hours free to earn some money, or someone with spare space in their warehouse.

The future competition will be between the quality of ecosystems that a logistics player operates in and not just other logistics players. These ecosystems will grow stronger, smarter, and larger with the participation of more players and the addition of extensive and diverse data sets. The Delhivery platform will be one of the first such ecosystems that will spur the real-world transition of domain actors to Supply Chain 4.0.

SEE ALSO: Drones Have Proved Their Utility During COVID-19 in India; Laxed Regulations Might Help Kickstart Industry

See more here:

COVID-19 And The Role Of AI, Machine Learning In Logistics: A Conversation With Delhivery CTO Kapil Bharati - Mashable India

Analyst expectations of firms earnings are on average biased upwards, and that bias varies over time and stocks, according to new research by experts at Wharton and elsewhere. They have developed a machine-learning model to generate a statistically optimal and unbiased benchmark for earnings expectations, which is detailed in a new paper titled, Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases. According to the paper, the model has the potential to deliver profitable trading strategies: to buy low and sell high. When analyst expectations are too pessimistic, investors should buy the stock. When analyst expectations are excessively optimistic, investors can sell their holdings or short stocks as price declines are forecasted.

[With the machine-learning model], we can predict how the prices of the stocks will behave based on whether or not the analyst forecast is too optimistic or too pessimistic, said Wharton finance professor Jules H. van Binsbergen, who is one of the papers authors. His co-authors are Xiao Han, a doctoral student at the University of Edinburgh Business School; and Alejandro Lopez-Lira, a finance professor at the BI Norwegian Business School.

The researchers found that the biases of analysts increase in the forecast horizon, or in the period when the earnings announcement date is not anytime soon. However, on average, analysts revise their expectations downwards as the date of the earnings announcement approaches. These revisions induce negative cross-sectional stock predictability, the researchers write, explaining that stocks with more optimistic expectations earn lower subsequent returns. At the same time, corporate managers have more information about their own firms than investors have, and can use that informational advantage by issuing fresh stock, Binsbergen and his co-authors note.

The Opportunity to Profit

Comparing analysts earnings expectations with the benchmarks provided by the machine-learning algorithm reveals the degree of analysts biases, and the window of opportunity it opens. Binsbergen explained how investors could profit from their machine-learning model. With our machine-learning model, we can measure the mistakes that the analysts are making by taking the difference between what theyre forecasting and what our machine-learning forecast estimates, he said.

We can measure the mistakes that the analysts are making by taking the difference between what theyre forecasting and what our machine-learning forecast estimates. Jules H. van Binsbergen

Using that arbitrage opportunity, investors could short-sell stocks for which analysts are overly optimistic, and book their profits when the prices come down to realistic levels as the earnings announcement date approaches, said Binsbergen. Similarly, they could buy stocks for which analysts are overly pessimistic, and sell them for a profit when their prices rise to levels that correspond with earnings that turn out to be higher than forecasted, he added.

Binsbergen identified two main findings of the latest research. One is how optimistic analysts are substantially over time. Sometimes the bias is higher, and sometimes it is lower. That holds for the aggregate, but also for individual stocks, he said. With our method, you can track over time the stocks for which analysts are too optimistic or too pessimistic. That said, there are more stocks for which analysts are optimistic than theyre pessimistic, he added.

The second finding of the study is that there is quite a lot of difference between stocks in how biased the analysts are, said Binsbergen. So, its not that were just making one aggregate statement, that on average for all stocks the analysts are too optimistic.

Capital-raising Window for Corporations

Corporations, too, could use the machine-learning algorithms measure for analysts biases. If you are a manager of a firm who is aware of those biases, then in fact you can benefit from that, said Binsbergen. If the price is high, you can issue stocks and raise money. Conversely, if analysts negative biases push down the price of a stock, they serve as a signal for the firm to avoid issuing fresh stock at that time.

When analysts biases lift or depress a stocks price, it implies that the markets seem to be buying the analysts forecasts and were not correcting them for over-optimism or over-pessimism yet, Binsbergen said. With the machine-learning model that he and his researchers have developed, you can have a profitable investment strategy, he added. That also means that the managers of the firms whose stock prices are overpriced can issue stocks. When the stock is underpriced they can either buy back stocks, or at least refrain from issuing stocks.

For their study, the researchers used information from firms balance sheets, macroeconomic variables, and analysts predictions. They constructed forecasts for annual earnings that are a year and two years ahead for annual earnings; similarly, they used forecasts that were one, two and three quarters ahead for quarterly earnings. With the benchmark expectation provided by their machine-learning algorithm, they then calculated the bias in expectations as the difference between the analysts forecasts and the machine-learning forecasts.

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How to Beat Analysts and the Stock Market with Machine Learning - Knowledge@Wharton

Artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used by Fintech platform developers to make more intelligent or informed decisions regarding key processes. This may include using AI to identify potentially fraudulent transactions, determining the creditworthiness of a borrower applying for a loan, and many other use cases.

Research conducted by Accenture found that 87% of business owners in the United Kingdom claim that theyre struggling with finding the best ways to adopt AI or ML technologies. Three out of four or 75% of C-Suite executives responding to Accentures survey said they really need to effectively adopt AI solutions within 5 years, so that they dont lose business to competitors.

As reported by IT Pro Portal, theres currently a gap between what may be considered just hype and actual or practical implementation of AI technologies and platforms.

Less than 5% of firms have actually managed to effectively apply Ai, meanwhile, more than 80% are currently just exploring basic proof of concepts for applying AL or ML algorithms. Many firms are also not familiar or dont have the expertise to figure out how to best apply these technologies to specific business use cases.

Yann Stadnicki, an experienced technologist and research engineer, argues that these technologies can play a key role in streamlining business operations. For example, they can help Fintech firms with lowering their operational costs while boosting their overall efficiency. They can also make it easier for a companys CFO to do their job and become a key player when it comes to supporting the growth of their firm.

Stadnicki points out that a research study suggests that company executives werent struggling to adopt AI solutions due to budgetary constraints or limitations. He adds that the study shows there may be certain operational challenges when it comes to effectively integrating AI and ML technologies.

He also mentions:

The inability to set up a supportive organizational structure, the absence of foundational data capabilities, and the lack of employee adoption are barriers to harnessing AI and machine learning within an organization.

He adds:

For businesses to harness the benefits of AI and machine learning, there needs to be a move away from an overhyped theoretical narrative towards practical implementation.It is important to formulate a plan and integration strategy of how your business will use AI and ML, to both mitigate the risks of cybercrime and fraud, while embracing the opportunity of tangible business impact.

Fintech firms and organizations across the globe are now leveraging AI and ML technologies to improve their products and services. In a recent interview with Crowdfund Insider, Michael Rennie, a U.K.-based product manager for Mendix, a Siemens business and the global leader in enterprise low-code, explained how emerging tech can be used to enhance business processes.

He noted:

Prior to low-code, the application and use of cutting-edge technologies within the banking sector have been more academic than actual. But low-code now enables you to apply emerging technologies like AI in a practical way so that they actually make an impact. For example, you could pair a customer-focused banking application built with low-code with a machine learning (ML) engine to identify user behaviors. Then you could make more informed decisions about where to invest in customer experience and most benefit your business.

He added:

Its easy to see the value in this. The problem is that without the correct technology, its too difficult to integrate traditional customer-facing applications with new technology systems. Such integrations typically require millions of dollars in investment and years of work. By the time an organization finishes that intensive work, the market may have moved on. Low-code eliminates that problem, makes integration easy and your business more agile.

. Bookmark the

.

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AI and Machine Learning Can Help Fintechs if We Focus on Practical Implementation and Move Away from Overhyped Narratives, Researcher Says - Crowdfund...

While the iShares fund has impressive average returns, performance from one year to the next has varied widely. In 2013, for example, the fund grew nearly 34% -- but then it showed a 0.78% loss in 2018.

That's the nature of growth funds; they can be a rollercoaster ride. Keep that in mind as you decide how to use this position. It could be appropriate for a long-term savings goal, like saving for retirement healthcare costs that you'll incur 25 years from now. But if you need the money within the next 10 years, you might want something more stable.

Planning for your future healthcare expenses is not an exact science. Even so, it is safe to assume that your medical costs will probably be the largest line item on your retirement budget. And that means you're smart to save and invest as much as you can now in your HSA. You can always adjust your plan later, but you can't go back and make up for earnings missed because you didn't start saving soon enough.

10 stocks we like better than iShares Russell 3000 Growth Index

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David and Tom just revealed what they believe are the ten best stocks for investors to buy right now... and iShares Russell 3000 Growth Index wasn't one of them! That's right -- they think these 10 stocks are even better buys.

The rest is here:
Cover Your Biggest Retirement Expense With $300 Monthly in This ETF - Waco Tribune-Herald

Ill be 57 next month and am divorced with three kids living with me. One is 28, shes working, another is 21 and a senior in college (with a full scholarship) and the youngest is 15 (a sophomore in high school with a full scholarship).

I plan to retire at the end of next year with $25,000 in credit card debt and 15 more years to pay my mortgage. The credit cards have 0% interest. I have a good medical benefit when I retire and it will cover my two sons under 26 years old. My monthly expenses are $2,000, including life insurance, utilities, and a car payment.

My mortgage is around $4,000 monthly impounded. The interest rate is 2% until January 2022, then 3% until January 2023 and the remaining loan is 4.5%. Is it worth it to refinance to a lower rate? I also plan to just pay the principal and pay interest in December and April. I have two credit cards: one that totals $20,000, where the 0% promo ends in April 2021, and another with $4,500 where the 0% interest promo ends this December.

I work for the state and have a pension and 401(k) and 457 investments that total $110,000. I also have one months worth of expenses in an emergency fund. I can only apply for a loan to the retirement accounts while employed.

I would like to ask if retiring will be a good idea. If so, is it appropriate to take a loan with my investment to pay off the credit card debt before retiring? Based on our benefit, I dont have to repay the debt (to the 401(k)) after my retirement unless I win the lottery or something. There wont be a penalty. My annual gross income is $96,000.

Im a cohabitant with my ex on the house but get no contribution from him at all. I am working with my lawyer to see if I have the right to kick him out of the house.

Please help.

Thank you.

CDT

See: Im a 57-year-old nurse with no retirement savings and I want to retire within seven years. What can I do?

Dear CDT,

You have a lot to juggle, so the fact that youre reaching out to someone for some financial guidance should be deemed an accomplishment all its own!

The truth is, you may want to hold off on retiring if you can. Having $110,000 in retirement accounts is great, and you dont want to have to start dwindling that down while also trying to manage a way to effectively pay down credit card debt and a mortgage. Should an emergency arise, taking a big chunk out of that nest egg could end up hurting you significantly in the long run.

I think she needs to take a hard look at her income and expenses, said Tammy Wener, a financial adviser and co-founder of RW Financial Planning. When it comes to retirement, so many things are out of your control, like inflation and investment return. The one thing you do have control over is expenses. Furthermore, your pension may be enough to maintain your lifestyle though advisers wondered what exactly you would be getting from that pension every month but you would still be better off with a larger nest egg to fall back on.

Say you retire next year after all, but you still have credit card debt and hefty bills to pay. Any retirement income you have with and outside of your current funds may not be sufficient for your current living expenses, and if in a few years you realize this, you could end up back in the workforce though it may be hard to get the same or a similar job you already have.

Lets look at your 401(k) and 457 plans for a moment. You said you could take a loan and based on your benefit you dont need to pay it back, but you should be extremely cautious about this. With 401(k) loans, employees may be required to repay that loan if theyre separated from their employers, so this is a stipulation you should absolutely verify. If there was any misunderstanding as to how a loan is treated, that remaining loan would be treated as taxable income when you left your job, Wener said.

Financial advisers usually caution investors not to take loans and withdrawals from retirement accounts if they can avoid it, and in your case, this may be especially true as you plan to retire in the next year. When you take a loan, you may be paying yourself and your account back, but your balance is reduced by the amount of the loan, which means you could lose out on investment returns. In the midst of this pandemic, many of the Americans who took a loan or withdrawal regret it now, a recent survey found. I would not recommend swapping debt by taking a loan from her investments, said Hank Fox, a financial planner. Instead, she should pay whatever amount is due each month to avoid the finance charges and continue to pay-down the balances.

Dont miss: 5 ways to find free financial advice

Also, consider what would happen if you continued to work: youd still be able to contribute to a retirement account, boost your savings and, if applicable, reap the rewards with an employer match. Youd also narrow the amount of time you have between retirement and when you can claim Social Security benefits, Fox said.

Outside of the retirement accounts, you should try to build a sizable emergency fund, Wener said. Financial advisers typically suggest three to six months worth of living expenses, though you might want to strive for closer to six to offset any undesirable scenarios.

Im not sure what the motivation was to retire next year, but if you can delay it, this may be the best solution. The first thing I would recommend is that she reconsider retiring next year, Fox said. Since she will be 57 in November and assuming she is in good health, she should expect to be in retirement for 30 years or more.

If postponing retirement is not an option, and it isnt always, he suggests reducing or eliminating your mortgage, since its your largest expense by far. You could refinance, Wener said. Interest rates are very low these days, and while you may end up paying a little more every month for the next two years compared with that 2% rate you currently have, youd end up paying the same and then less from February 2022 and on.

As for your credit cards, having a 0% interest rate is such a huge help in paying off debts faster, so you should try to extend that benefit, either by calling and asking about your options with your current credit card company or looking at alternative 0% interest cards.

A financial adviser specifically, a Certified Financial Planner could really help you crunch the numbers and find meaningful ways to make the most of the money you have now and will be getting in retirement, said Vince Clanton, principal and investment adviser representative at Chancellor Wealth Management.

An adviser can gather information on your current earnings and expenses, retirement savings, potential Social Security benefits and pension and create a financial plan to help you navigate retirement. Voluntary retirement, and particularly early retirement, are very big decisions, Clanton said. Its extremely important to know and understand all of the variables.

Letters are edited for clarity.

Have a question about your own retirement savings? Email us at HelpMeRetire@marketwatch.com

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I want to retire next year, but I have $25,000 in credit card debt and a major monthly mortgage payment I also live with my three kids and ex -...

Retirement savers may never have had it more difficult.

For decades, financial advisors routinely recommended an investment portfolio of 60% stocks and 40% bonds. It was seen as a goldilocks formula, combiningpotential for growth with protection if stock prices fell.

But the interest rate on benchmark 10-year US government bondslong a core part of the everyday persons retirement portfoliois hovering near record lows at around 0.8%, with little chance of rising anytime soon. The Federal Reserve, seeking to give the economy a boost, is likely to keep yields near zero until at least 2023.

To be ready for retirement, most people are going to have to find a way to save more, take more investment risk, or work longer than they might have expected. The famed 60-40 portfolio, which has returned about 10% a year since the depths of the 2008 financial crisis, may generate returns of about half that in the coming years according to some projections.

This is the hardest investing environment a lot of people have ever had to face, says Ben Carlson, director of institutional asset management at Ritholtz Wealth Management. At our firm, weve racked our brains forever about thiswhats the alternative? Is it dividend stocks? Is it corporate bonds, is it emerging market bonds? There really is no easy answer.

Treasury bonds have long performed an important job for investorstheyre pretty much the only asset that tends to increase in value when theres a panic, whether thats from a terrorist attack or a financial crisis. Giulio Renzi Ricci, senior investment strategist at Vanguard, says Treasuries still have a role to play as a shock absorber, and he continues to expect US government debt to be a haven when times get tough.

But once you account for inflation, government bonds have always been vulnerable to losing money. In inflation-adjusted terms, (using the CPI index, for example), 10-year Treasury securities are in negative territory, as theyve often been historically. And with the notes currently yielding less than 1%, their capacity for absorbing shocks has been greatly diminished.

Even a small drop in investment returns has immense impact onwhen, or whether, people can afford to retire. Imagine a 35-year-old investor who has socked away $50,000 and plans to put away $500 a month for the next 30 years. An annual return of 7% would result in a retirement portfolio worth more than $1 million when the person is 65. An annual return of 4% would result in holdings worth about half of that, around $513,000plus, three decades of inflation will eat into those returns, raising the risk that the person outlives their savings.

Finance professionals have mixed opinions on where to go from here.

As far as Jared Woodard is concerned, the 60-40 portfolio is dead. I came out with this thesis a year ago, and Im more confident than I was before, said Woodard, who heads the research investment committee at Bank of America.

He argues that such a conservative portfolio has pretty much always been a bad deal for investors: a dollar invested the entire US stock market in 1950 would be worth $1,763 now, while a dollar in a 60-40 portfolio would have only risen to about $535.

If investors cant afford to save more, they may instead dial up the risk, pushing more of their holdings into stocks (an 80-20 portfolio, perhaps), or swapping government bonds for corporate bonds and loans, emerging-market assets, or longer maturity debt. (Woodard thinks savers can take prudent risks by investing some of their money in things like corporate bonds and loans, higher yielding municipal debt, and even tech stocks, which have been a crowded trade for some time but remain one of the few places to find companies with decent earnings growth.)

But taking more risk has, well, risks. In swapping out Treasuries for riskier securities, youre diluting your diversification benefits, Vanguards Ricci says. Youre getting more expected return but for a higher level of risk.

If the stock market tanks just before a person retires, they might not be able to ride out the losses before theyre due to stop working.

Surprisingly, even as the hope for returns dwindles, investors in the US dont appear to be giving up on bonds. So far this year theyve put almost $20 billion into exchange-traded funds for Treasury debt, though thats on pace for the lowest annual total in four years, according to Eric Balchunas, an analyst at Bloomberg Intelligence. Corporate bond ETFs, meanwhile, have sucked in more than triple that amount. (Much of that money was probably trying to get in ahead of the Fed, which bought company debt ETFs for the first time ever. The central bank did so to keep the coronavirus pandemic from disrupting corporate financing markets.)

With Treasuries yielding so little, some investors have looked to gold as their crisis hedge. Gold ETFs have taken in $32.5 billion this year, blowing away the old annual record of $12 billion.

Research suggests that a lot of Americans are fairly risk-averse when it comes to their savings. In a Wells Fargo/Gallup survey about investment and retirement optimism, only 4% of the respondents said they were comfortable taking a lot of risk, while 46% said they wanted to take only a little risk. When it comes to investing, 59% said the fear of losing money was the emotion that had the biggest influence on their tolerance for risk.

If investors cant afford to save more and arent willing to dial up the risk, then they may plan to work longer. Indeed, almost 30% of people surveyed by financial advisor Edward Jones said they plan to delay retirement because of the coronavirus pandemic.

The trouble with this strategy is that people can overestimate their capacity to keep working. The average retirement age in the US is 61 and has barely budged in recent years. Thats five years younger than the age at which people, on average, expect to retire, according Gallup survey data of adults over age 18. Some may leave the workforce earlier than expected because of health problems, or to a look after a family member.

Older workers who lose or leave their jobs may find it difficult to find employment again, sometimes because of age discrimination. In this recession, customer-facing jobs in the retail or hospitality sectors may not be an option for older workers because they face greater Covid-19 health risks than younger employees.

I suspect a lot of aspiring retirees think theyre going to be able to work longer than theyre able to, said Robert Williams, a vice president at the Schwab Center for Financial Research.

Not saving enough for retirement has long been a problem for many Americans, but Williams says ultra-low interest rates are shining a light on this concern. (In his view, the 60-40 portfolio isnt dead, but he argues the investment staple needs to be adjusted, potentially with company debt instead of government debt, or more exposure to the stock market.)

Ritholtzs Carson, meanwhile, suspects ultra-low interest rates will encourage financial institutions to start marketing exotic stufffinancial products that use derivatives or leverage, as demand grows for anything with a hint a extra yield. Mostly, though, he expects that many peopleare just going to have to work longer, whether they want to or not, and rely pretty heavily on Social Security.

Continued here:
With interest rates near record lows, retirement may have to wait - Quartz

Maurie Backman, The Motley Fool Published 6:00 a.m. ET Oct. 16, 2020

Health care is a major burden for Americans of all ages. For seniors, it's a giant concern. Many seniors live on a fixed income and tight budget, relying heavily on their Social Security benefits to make ends meet. It's not surprising to learn that 60% of seniors 65 and older who are enrolled in Medicare worry about their ability to afford health care, according to a MedicareGuide.com survey. In fact, 50% of people in that age group fear that a major personal health crisis could lead to serious debtor even bankruptcy.

What's equally concerning is that 24% of older Americans say they'd need to use a credit card to pay for a severe illness. Meanwhile, 32% say they'd tap their retirement savings to cover that cost. The latter isn't terrible per se the whole point of having money in an IRA or 401(k) is to be able to spend it on any retirement expense that arises, health care included. But there's actually a better way for seniors to pay for health care and avoid debt at a time in their lives when they really can't afford it.

While padding an IRA or 401(k) during your working years could help ensure that you have enough money to pay for your future health-related needs, there's an even better account for that purpose: the health savings account.

An HSA actually offers more tax benefits than an IRA or a 401(k). Your contributions go in tax-free, the growth is tax-free, and the withdrawals are tax-free (provided they're used to cover qualified medical expenses).

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401 (k): A $5,500 withdrawal from your 401(k) could cost you $30,000

The beauty of HSAs is that their funds don't expire. You can contribute to an HSA at age 30 and withdraw money year by year as needed to pay for your near-term medical expenses. Any money you don't use can be invested and withdrawn later on. In fact, it pays to overfund your HSA year after year, putting in more money than you expect to use immediately so you have the option of carrying funds all the way into retirement. Having an HSA at that stage of life could spare you from debt or bankruptcy in the event of a serious illness or expensive hospital stay.

(Photo: Getty Images)

Shockingly, in the aforementioned survey, only 2% of respondents said they'd pay for a severe illness with HSA funds, suggesting many retirees today don't have one of these accounts at their disposal. If you have the option to participate in an HSA, it pays to not only take advantage, but to also contribute the maximum amount allowed.

Not sure how much to contribute to your 401(k)?: Make sure to get your full employer match.

For the current year, you can contribute up to $3,550 to an HSA for individual coverage and up to $7,100 for family coverage. Next year, these limits will increase to $3,600 and $7,200, respectively. If you're 55 or over, you can contribute an extra $1,000 on top of whichever limit applies to you.

Serious illnesses or injuries can strike at any time. In fact, 32% of seniors say they've had to grapple with a surprise medical bill over the past two years. The best way to tackle all your health care expenses in retirement is to have a dedicated source of funds at the ready to pay for them. If you play your cards right, an HSA could be your ticket to financial security even as your peers risk severe debt or bankruptcy.

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The Motley Fool is a USA TODAY content partner offering financial news, analysis and commentary designed to help people take control of their financial lives. Its content is produced independently of USA TODAY.

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60% of Medicare enrollees worry about health care costs. Here's the best way to pay for them in retirement - USA TODAY