Mediaboss Marketing

Equipping Financial Controllers with predictive capabilities, advanced analytics and ML will help them elevate their role from providing back-office support to business partnering.

The role of a finance controller is changing. It is expected that controllers will not only take ownership of the companys accounts but also drive strategic performance. Such change in role is further accentuated with the explosion in the volume and variety of data available with an organization. Furthermore, data landscape in organizations is becoming more and more siloed, complex and distributed. Given this shift in business dynamics, it is becoming extremely important to upskill on how advanced analytics, AI/ML techniques be leveraged to become an effective business partner driving performance in an organization.

Use-cases of AI/ML in Finance

Here a a number of use cases of how data science and ML techniques can be used in the business context to drive productivity and performance in the organization:

1)Identifying and preventing Revenue leakages: Revenue leakage is a major issue with many large enterprise and A/R leaders spent a substantial time and effort in preventing them. This could be due to multiple reasons viz. a process issue with disjointed systems, poor experience of customer, disputes, invalid deductions by customer with a relatively high volume and low value, auto-approved write-offs etc. Here, advanced analytics can play a significant role the root cause of such leakages and provide insights to the A/R team on actions that can be taken to prevent such instances.

For example, there have been instances where few customers use low dollar value deductions as a strategy to strengthen their cash flow. In such situations, it is difficult to track low-dollar value deductions as it is really a small number and is below the acceptable tolerance / threshold. This becomes a scenario of finding a needle in a haystack. However, when such deductions are aggregated at a customer level over a period of time, it can be truly amazing to seehow certaingroup of customers are actually using this strategy to cause a significant cash flow leakage for the company. To track such events, there are advanced clustering algorithms which can provide which customers are consistently using this strategy and can help the A/R team to go and recover them.

2)Identifying high risk customers and undertaking recommended actions for faster collection:For organizationshaving thousands of transactions across a large customer, it is really a difficult task to understand the behavior and financial stability of its customer due to which there are late payments or sometimes the receivables are written off. To avoid such scenarios, advanced classification algorithms can help detect such customer at risk and help organization to take pro-active steps to not only identify the customers but also reduce exposures to them over a period of time. In order to implement such smart solutions, it is really important to have the finance leader defining the key variables or data points needed to develop such classification algorithm which then the data scientist will use in its modelling. In other words, it needs a close co-operation between the finance leaders and Data scientist to model the key variables and scenarios.

3)Inventory Management: Inventory management is a major challenge in an organization. There are different categories of inventory Finished goods, semi-finished goods, raw material etc. and within each of these categories, there could be different types viz. slow moving, fast moving etc. The use of AI/ML can help manage inventory by revealing insightful information about Stock keeping units (SKU) and their associated variables such as minimum order quantity, lead times, replenishment frequency, and safety stocks. Using predictive capabilities, advanced classification algorithms can help to keep the inventory issues such as supply mismanagement, deadstock, and wastage under strict control.

4)Improving Cash Conversion / working capital: One of the significant benefits of AI/ML is the optimization of cash conversion cycles by optimizing the management of receivables, inventory and payables. This, in turn, helps the company to perform well on the cash conversion and significantly improve its performance on accounts receivables.

5)Intelligent Root cause Analysis: The use of AI/ML offers profoundly important information on various business scenarios that could possibly spring in the future as a result of changing business environments. Whether it is the use of predictive analysis, scenario modeling, or descriptive root cause, AI/ML can help financial controllers in understanding the main reasons why some of the product gained immense popularity while others fail to find favor with consumers.

There is little to doubt about the transformative power of AI/ML. These solutions can transform the role of financial controllers and can catapult their positions to one of strategic relevance to the company. That said, with a plethora of choices around, financial controllers should opt for holistic and comprehensive solutions so that the benefits of AI/ML solutions can be realized in a holistic manner.

Views expressed above are the author's own.

END OF ARTICLE

Read more here:
How advanced analytics and machine learning are transforming the role of Finance Controllers - Times of India

Anima Anandkumar, Bren Professor of computing at the California Institute of Technology and senior director of machine learning research at Nvidia, has a bone to pick with the matrix. Her misgivings are not about the sci-fi movies, but about mathematical matrices grids of numbers or variables used throughout computer science. While researchers typically use matrices to study the relationships and patterns hiding within large sets of data, these tools are best suited for two-way relationships. Complicated processes like social dynamics, on the other hand, involve higher-order interactions.

Luckily, Anandkumar has long savored such challenges. When she recalls Ugadi, a new years festival she celebrated as a child in Mysore (now Mysuru), India, two flavors stand out: jaggery, an unrefined sugar representing lifes sweetness, and neem, bitter blossoms representing lifes setbacks and difficulties. Its one of the most bitter things you can think about, she said.

Shed typically load up on the neem, she said. I want challenges.

This appetite for effort propelled her to study electrical engineering at the Indian Institute of Technology in Madras. She earned her doctorate at Cornell University and was a postdoc at the Massachusetts Institute of Technology. She then started her own group as an assistant professor at the University of California, Irvine, focusing on machine learning, a subset of artificial intelligence in which a computer can gain knowledge without explicit programming. At Irvine, Anandkumar dived into the world of topic modeling, a type of machine learning where a computer tries to glean important topics from data; one example would be an algorithm on Twitter that identifies hidden trends. But the connection between words is one of those higher-order interactions too subtle for matrix relationships: Words can have multiple meanings, multiple words can refer to the same topic, and language evolves so quickly that nothing stays settled for long.

This led Anandkumar to challenge AIs reliance on matrix methods. She deduced that to keep an algorithm observant enough to learn amid such chaos, researchers must design it to grasp the algebra of higher dimensions. So she turned to what had long been an underutilized tool in algebra called the tensor. Tensors are like matrices, but they can extend to any dimension, going beyond a matrixs two dimensions of rows and columns. As a result, tensors are more general tools, making them less susceptible to overfitting when models match training data closely but cant accommodate new data. For example, if you enjoy many music genres but only stream jazz songs, your streaming platforms AI could learn to predict which jazz songs youd enjoy, but its R&B predictions would be baseless. Anandkumar believes tensors make machine learning more adaptable.

Its not the only challenge shes embraced. Anandkumar is a mentor and an advocate for changes to the systems that push marginalized groups out of the field. In 2018, she organized a petition to change the name of her fields annual Neural Information Processing Systems conference from a direct acronym to NeurIPS. The conference board rejected the petition that October. But Anandkumar and her peers refused to let up, and weeks later the board reversed course.

Quanta spoke with Anandkumar at her office in Pasadena about her upbringing, tensors and the ethical challenges facing AI. The interview has been condensed and edited for clarity.

In the early 1990s they were among the first to bring programmable manufacturing machines into Mysore. At that time it was seen as something odd: We can hire human operators to do this, so what is the need for automation? My parents saw that there can be huge efficiencies, and they can do it a lot faster compared to human-operated machines.

Yeah. And programming. I would see the green screen where my dad would write the program, and that would move the turret and the tools. It was just really fascinating to see understanding geometry, understanding how the tool should move. You see the engineering side of how such a massive machine can do this.

My mom was a pioneer in a sense. She was one of the first in her community and family background to take up engineering. Many other relatives advised my grandfather not to send her, saying she may not get married easily. My grandfather hesitated. Thats when my mom went on a hunger strike for three days.

As a result, I never saw it as something weird for women to be interested in engineering. My mother inculcated in us that appreciation of math and sciences early on. Having that be just a natural part of who I am from early childhood went a long way. If my mom ever saw sexism, she would point it out and say, No, dont accept this. That really helped.

The rest is here:
The AI Researcher Giving Her Field Its Bitter Medicine - Quanta Magazine

AUSTIN, Texas Sharing news articles with friends and followers on social media can prompt people to think they know more about the articles topics than they actually do, according to a new study from researchers at The University of Texas at Austin.

Social media sharers believe that they are knowledgeable about the content they share, even if they have not read it or have only glanced at a headline. Sharing can create this rise in confidence because by putting information online, sharers publicly commit to an expert identity. Doing so shapes their sense of self, helping them to feel just as knowledgeable as their post makes them seem.

This is especially true when sharing with close friends, according to a new paper from Susan M. Broniarczyk, professor of marketing, and Adrian Ward, assistant professor of marketing, at UTs McCombs School of Business.

The research isonline in advance in the Journal of Consumer Psychology. The findings are relevant in a world in which its simple to share content online without reading it. Recent data from the Reuters Institute for the Study of Journalism show only 51% of consumers who read an online news story actually read the whole article, while 26% read part, and 22% looked at just the headline or a few lines.

Broniarczyk, Ward and Frank Zheng, a McCombs marketing doctoral alum, conducted several studies that support their theory. In an initial one, the researchers presented 98 undergraduate students with a set of online news articles and told them they were free to read, share, or do both as they saw fit. Headlines included Why Does Theatre Popcorn Cost So Much and Red Meats Linked to Cancer.

Next, they measured participants subjective and objective knowledge for each article what the students thought they knew, and what they actually knew. Reading articles led to increases in both objective and subjective knowledge. Sharing articles also predicted increases in subjective knowledge even when students had not read what they chose to share, and thus lacked objective knowledge about the articles content.

In a second study, people who shared an article about cancer prevention came to believe they knew more about cancer than those who did not, even if they had not read the article.

Three additional studies found this effect occurs because people internalize their sharing into the self-concept, which leads them to believe they are as knowledgeable as their posts make them appear. Participants thought they knew more when their sharing publicly committed them to an expert identity: when sharing under their own identity versus an alias, when sharing with friends versus strangers, and when they had free choice in choosing what to share.

In a final study, the researchers asked 300 active Facebook users to read an article on How to Start Investing: A Guide for Beginners. Then, they assigned students to a sharing or no sharing group. All participants were told the content existed on several websites and saw Facebook posts with the sites. Sharers were asked to look at all posts and choose one to share on their Facebook page.

Next, in a supposedly unrelated task, a robo-advised retirement planning simulation informed participants that allocating more money to stocks is considered more aggressive and to bonds more conservative, and they received a customized investment recommendation based on their age. Participants then distributed a hypothetical $10,000 in retirement funds between stocks and bonds: Sharers took significantly more investment risk. Those who shared articles were twice as likely to take more risk than recommended by the robo-advisor.

When people feel theyre more knowledgeable, theyre more likely to make riskier decisions, Ward said.

The research also suggests theres merit to social media companies that have piloted ways to encourage people to read articles before sharing.

If people feel more knowledgeable on a topic, they also feel they maybe dont need to read or learn additional information on that topic, Broniarczyk said. This miscalibrated sense of knowledge can be hard to correct.

For more details about this research, read the McCombs Big Ideas feature story and watch the video explaining Broniarczyk and Wards work.

See the article here:
Sharing on Social Media Makes Us Overconfident in Our Knowledge - UT News - The University of Texas at Austin

This article is part of our series that explores thebusiness of artificial intelligence

Applied artificial intelligence tops the list of 14 most influential technology trends in McKinsey & Companys Technology Trends Outlook 2022 report.

For now, applied AI (which might also be referred to as enterprise AI) is mainly the use of machine learning and deep learning models in real-world applications. A closely related trend that also made it to McKinseys top-14 list is industrializing machine learning, which refers to MLOps platforms and other tools that make it easier to train, deploy, integrate, and update ML models in different applications and environments.

McKinseys findings, which are in line with similar reports released by consulting and research firms, show that after a decade of investment, research, and development of tools, the barriers to applied AI are slowly fading.

Large tech companies, which often house many of the top machine learning/deep learning scientists and engineers, have been researching new algorithms and applying them to their products for years. Thanks to the developments highlighted in McKinseys report, more organizations can adopt machine learning models in their applications and bring their benefits to their customers and users.

The recent decade has seen a revived and growing mainstream interest in artificial intelligence, mainly thanks to the proven capabilities of deep neural networks in performing tasks that were previously thought to be beyond the limits of computers. During the same period, the machine learning research community has made very impressive progress in some of the challenging areas of AI, including computer vision and natural language processing.

The scientific breakthroughs in machine learning were largely made possible because of the growing capabilities to collect, store, and access data in different domains. At the same time, advances in processors and cloud computing have made it possible to train and run neural networks at speeds and scales that were previously thought to be impossible.

Some of the milestone achievements of deep learning were followed by news cycles that publicized (and often exaggerated) the capabilities of contemporary AI. Today, many companies try to present themselves as AI first, or pitch their products as using the latest and greatest in deep learning.

However, bringing ML from research labs to actual products presents several challenges, which is why most machine learning strategies fail. Creating and maintaining products that use machine learning requires different infrastructure, tools, and skill sets than those used in traditional software. Organizations need data lakes to collect and store data, and data engineers to set up, maintain, and configure the data infrastructure that makes it possible to train and update ML models. They need data scientists and ML engineers to prepare the data and models that will power their applications. They need distributed computing experts that can make ML models run in a time- and cost-efficient manner and at scale. And they need product managers who can adapt the ML system to their business model and software engineers who can integrate the ML pipeline into their products.

The data, hardware, and talent costs that come with enterprise AI have been often too prohibitive for smaller organizations to make long-term investments in ML strategies.

It is against this backdrop that the McKinsey & Company reports findings are worth examining.

The report ranks tech trends based on five quantifiable measures: search engine queries, news publications, patents, research publications, and investment. It is worth noting that such quantitative measures dont always paint the most accurate picture of the relevance of a trend. But tracking them over time can give a good estimate of how a technology goes through the different steps of hype, adoption, and productivity cycle.

McKinsey further corroborated its findings through surveys and interviews with experts from 20 different industries, which gives a better picture of what the opportunities and challenges are.

The report is based on 2018-2021 data, which does not fully account for the downturn that capital markets are currently undergoing. According to the findings, applied AI has seen growth in all quantifiable measures except for the search engine queries category (which is a grey area, since AI terms and trends are constantly evolving). McKinsey gives applied AI the highest innovation score and top-five investment score with $165 billion in 2021.

(Measuring investment is also very subjective and depends on how you define applied AIe.g., if a company that secures a huge round of funding uses machine learning as a small part of its product, will it count as an investment in applied AI?)

In terms of industry relevance, some of the ML applications mentioned in the report include use cases such as recommendation engines (e.g., content recommendation, smart upselling), detection and prevention (e.g., credit card fraud detection, customer complaint modeling, early disease diagnosis, defect prediction), and time series analysis (e.g., managing price volatility, demand forecasting). Interestingly, these are some of the areas of machine learning where the algorithms have been well-developed for years. Though computer vision is only mentioned once in the use cases, some of the applications might benefit from it (e.g., document scanning, equipment defect detection).

The report also mentions some of the more advanced areas of machine learning, such as generative deep learning models (e.g., simulation engines for self-driving cars, generating chemical compounds), transformer models (e.g., drug discovery), graph neural networks, and robotics.

This further drives the point that the main hurdle for the adoption of applied AI has not been poor machine learning algorithms but the lack of tooling and infrastructure to put well-known and -tested algorithms to efficient use. These constraints have limited the use of applied AI to companies that dont have enormous resources and access to scarce machine learning talent.

In recent years, there has been tremendous advances in some of these fronts. Weve seen the advent and maturity of no-code ML platforms, easy-to-use ML programming libraries, API-based ML services (MLaaS), and special hardware for training and running ML models. At the same time, the data storage technologies underlying ML services have evolved to become more flexible, interoperable, and scalable. Meanwhile, some enterprise AI companies have started to develop and provide ML solutions for specific sectors (e.g., financial services, oil and gas, retail).

All these developments reduce the financial and technical barriers to adopting machine learning in their business models. In many cases, companies can integrate ML services into their applications without having in-depth knowledge of the algorithms running in the background.

According to McKinseys 2021 survey of industry experts, 56 percent of respondents said their organizations had adopted AI, up from 50 percent in the 2020 survey. The 2021 survey also indicated that adopting AI can have financial benefits: 27 percent of respondents attributed 5 percent or more of their companies EBIT to AI.

The second AI-related tech trend included in the McKinsey & Company report is the industrialization of machine learning. This is a vague term and has much overlap with the applied AI category, so the report defines it as an interoperable stack of technical tools for automating ML and scaling up its use so that organizations can realize its full potential.

The technologies underlying advances in this field are mostly the same that have led to the growth of applied AI (better data storage platforms, hardware stacks, ML development tools and platforms, etc.). However, one specific field that has seen impressive developments in recent years is machine learning operations (MLOps), the set of tools and practices that streamline the training, deployment, and maintenance of ML models.

MLOps platforms provide tools for curating, processing, and labeling data; training and comparing different machine learning models; versioning control for dataset and models; deploying ML models and monitoring their performance; and updating ML models as their performance decays, their environment changes, and new data becomes available. MLOps platforms, which are growing in number and maturity, bring together several different tasks that were previously carried out desperately and in an ad hoc fashion.

According to the report, the industrialization of machine learning can shorten the production time frame for ML applications by 90 percent (from proof of concept to product) and reduce development resources by up to 40 percent.

Despite the advances in applied AI, the field still has some gaps to bridge. The McKinsey report states that the availability of resources such as talent and funding remain two of the hurdles for the further growth of enterprise AI. Currently, the capital markets are in a downturn, and all sectors, including AI, are facing problems funding their startups and companies.

However, despite the AI capital pie becoming smaller, funding has not stopped altogether. According to a recent CB Insights report, companies that have already achieved product/market fit and are ready for aggressive growth are still managing to secure mega-funding rounds (above $100 million). This suggests that companies that dont have the margins to launch new ML strategies will have a hard time receiving outside funding. But applied ML platforms that have already cornered their share of the market will continue to draw interest from investors.

Another important challenge that the report mentions is data risks and vulnerabilities. This is becoming an increasingly critical issue for applied machine learning. Like its development lifecycle, the security threat landscape of machine learning is different from that of traditional software. The security tools used in most software development platforms are not designed to detect adversarial examples, data poisoning, membership inference attacks, and other types of threats against ML models.

Fortunately, the security and machine learning communities are coming together to develop tools and practices for creating secure ML pipelines. As applied AI continues to grow, we can expect other sectors to speed up their adoption of ML, which will in turn further accelerate the pace of innovation in the field.

Here is the original post:
The growth stage of applied AI and MLOps - TechTalks

Mlily USA has developed an easy-to-use guide for retailers looking to be more effective with social media and marketing.

KNOXVILLE, Tenn. In a move designed to help boost its retail partners social media and marketing prowess, bedding supplier Mlily USA has developed an easy-to-use guide that offers how-tos and best practices for establishing a vibrant social media presence.

The complimentary guide offers instructions for setting up a social media profile, creating and sharing posts, and tagging. It covers four of the most widely used social media platforms: Facebook, Instagram, Twitter and LinkedIn.

Social media is an essential part of retail businesses to reach a vast audience, increase brand awareness and attract new customers, said Ryan Farber, vice president of marketing. Our primary goal with the social media guide is to support our retailers and meet them where they are. When it comes to social media, most of us, from novice to expert, can use support because it evolves quickly, and every channel has its own set of features and nuances.

Available through Mlilys partner portal as an interactive PDF, the guide includes a section on brand terminology, materials and technology, as well as access to other resources such as product information and visual assets.

Farber said the idea was derived from the companys sales and marketing teams that were spending time explaining the benefits of social media and the impact it could have on business.

Im Sheila Long OMara, executive editor at Furniture Today. Throughout my 25-year career in the home furnishings industry, I have been an editor with a number of industry publications and spent a brief stint with a public relations agency where I worked with some of the industrys leading bedding brands. I rejoined Furniture Today in December 2020 with a focus on bedding and sleep products. Its a homecoming for me, as I was a writer and editor with Furniture Today from 1994 until 2002. Im happy to be back and look forward to telling the important stories impacting bedding retailers and manufacturers.

More:
Hunting for ways to boost your social media presence? This mattress brand is ready to help - Furniture Today