The green-eyeshade public servants at the Treasury Department have dealt with large amounts of money for decades. But as the size of their mission has grown exponentially over the years, the never-ending repetitive tasks they tackle have increasingly relied on artificial intelligence and machine learning.

The folks at the Bureau of the Fiscal Service are well aware of how technology leads to faster and more accurate results.

“The Bureau of the Fiscal Service, I would say, is really kind of the operational arm of accounting in the federal government,” said Adam Goldberg, the Treasury Department’s Acting Assistant Commissioner of Financial Innovation and Transformation at the Bureau of Fiscal Service, on Federal Monthly Insights – Repurposing Manpower Through Automation.

The Bureau issues checks to people and businesses, as well as collects funds for federal agencies.

“We finance the government through our auctions and retail security program,” said Goldberg. “And we issue things like the government wide financial statements. They are very practical and pragmatic things to let the government operate on a daily basis.”

Goldberg sees the Bureau generate large volumes of things, which would only be possible through automation.

“Everything today is digital, so all of our capabilities need to align with that,” said Goldberg on Federal Drive with Tom Temin.

As the Bureau constantly strives for ways to be more efficient for taxpayers, that efficiency is made possible with AI, machine learning, and robotic process automation.

The post Accounting for big dollars has Treasury embracing AI and machine learning appeared first on Artificial Intelligence.

Being able to deploy machine learning applications at the edge is the key to unlocking a multibillion-dollar market. TinyML is the art and science of producing machine-learning models frugal enough to work at the edge, and it’s seeing rapid growth.

Is it $61 billion and 38.4% compound annual growth rate (CAGR) by 2028 or $43 billion and 37.4% CAGR by 2027? Depends on which report outlining the growth of edge computing you choose to go by, but in the end it is not that different.

What matters is that edge computing is booming. There is growing interest by vendors, and ample coverage, for good reason. Although the definition of what constitutes edge computing is a bit fuzzy, the idea is simple. It is about taking compute out of the data center and bringing it as close to where the action is as possible.

Whether it’s stand-alone Internet-of-things sensors, devices of all kinds, drones, or autonomous vehicles, there’s one thing in common. Increasingly, data generated at the edge are used to feed applications powered by machine learning models. There’s just one problem: machine learning models were never designed to be deployed at the edge. Not until now, at least. Enter TinyML.

Tiny machine learning (TinyML) is broadly defined as a fast-growing field of machine-learning technologies and applications including hardware, algorithms, and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, hence enabling a variety of always-on use-cases and targeting battery-operated devices.

This week, the inaugural TinyML EMEA Technical Forum is taking place, and it was a good opportunity to discuss with some key people in this domain. ZDNet caught up with Evgeni Gousev from Qualcomm, Blair Newman from Neuton, and Pete Warden from Google.

Hey Google
Pete Warden wrote the world’s only mustache-detection image-processing algorithm. He also was the founder and chief technology officer of startup Jetpac. He raised a Series A from Khosla Ventures, built a technical team, and created a unique data product that analyzed the pixel data of more than 140 million photos from Instagram and turned them into in-depth guides for more than 5,000 cities around the world.

Jetpac was acquired by Google in 2014, and Warden has been a Google Staff Research Engineer since. Back then, Warden was feeling pretty good about himself for being able to fit machine-learning models in 2 megabytes.

That was until he found some of his new Google colleagues had a 13 kilobyte model that they were using to recognize wake words running on always-on digital signal processor
on Android devices. That way the main CPU wasn’t burning battery listening out for “that” wake word—Hey Google.

“That really blew my mind, the fact that you could do something actually really useful in that smaller model. And it really got me thinking about all of the other applications that might be possible if we can run especially all these new machine-learning, deep-learning approaches” Warden said.

Although Warden is oftentimes credited by his peers as having kickstarted the TinyML subdomain of machine learning, he is quite modest about it. Much of what he did, he acknowledges, was based off things others were already working on: “A lot of my contribution has been helping publicize and document a bunch of these engineering practices that have emerged,” he said.

The post Machine Learning at the Edge: TinyML Is Getting Big appeared first on Artificial Intelligence.

AI in fraud investigation makes the whole process efficient by generating relevant data

Right from deploying machines to get the work done to the robots assisting the doctors in surgeries, we’ve come a long way – thanks to Artificial Intelligence, truly a remarkable innovation! Today, the business models that we get to see extensive use of technology. Also, the new and complex challenges behind managing the fraud investigation are a strenuous task in itself. Well, it doesn’t end there. Cross-border probe adds to the already existing complexity. Such an investigation could highlight bribery, corruption, data breach, conflict of interest, fraud in financial reporting and IP theft, to name a few.

There are a lot of factors that need to be accounted in case of a cross-border probe. Some of them are –

• Local rules, laws and regulations
• Cultural attributes
• Language barriers
• Different standardization levels, etc.

No wonder why such a probe is complex and full of challenges. With these complexities stepping in, deploying the right tools with a well-laid investigation methodology standardisation is the need of the hour.

The procedure followed by such a probe is as stated –

• Needless to say, the first step has to be getting in as much information as possible. Relying on both – external as well as internal sources for the same yields fruitful results. External sources include media, open source information, etc. whereas internal sources obviously revolves around employees, vendors, business operations, etc. A key point to note is that the information collected should be on a timely basis and also from as many sources as possible.
• Next up, try identifying the relationship between key entities and individuals
• Many tend to ignore this but is equally important – a sound knowledge on the event chronology.
• Transactional data holds a lot of crucial information. Being able to understand what goes in and how to draw necessary insights is the key here. The probe is incomplete when transactional data is not being addressed.

Now that you have a pile of data to analyse, sitting to scan every bit of this makes no sense. Identifying relevant content is the key here. It is here that Artificial Intelligence comes into the picture. Embedded artificial intelligence helps in filtering down the content and classifying it as required. The feature of semantic search is no less than a blessing here for it automatically identifies related concepts and documents.

Artificial intelligence in fraud investigation makes the whole process efficient by generating relevant data and leaves us in a position to draw meaningful insights.

Data extraction is a tedious task and when sensitive data like the data pertaining to banks, financial institutions, hospitals, etc. is involved, one cannot afford being negligent here. With advanced computer vision algorithms in place, it is possible to extract information from bank statements, and various other documents. Natural Language Processing (NLP) techniques help in extracting information and also aid in performing automated verification using digital channels. Artificial intelligence is no less than a saviour for financial institutions as it caters to verification of the critical details and documents.

Analytics also plays a pivotal role in investigation. Two types of analytics, namely advanced analytics and process analytics have a lot to offer to ease the investigation process. Talking about advanced analytics, it performs the following –

1. Network analysis: This is where a relationship between individuals and entities is established.

2. Sentiment analysis: When documents are to be differentiated on the basis of tone, subject, etc., analytics comes into play. Also, any suspicious review, article, conversation, etc. can be effectively identified.

3. Detecting anomalies in the transactional data is easier than ever.

4. Using AI, it is also possible to identify undisclosed entities.

Process analytics: What can get better than getting to know how your processes are performing in addition to what needs to be done to perform better? This is exactly what process analytics has in store for you!

Though these tools and technologies can help in fraud investigation and management, what needs to be understood here is that the process is complex and comes with their own challenges. Sound knowledge about these tools and techniques might help. With frauds and crimes in the world of technology continue to rise, it is high time that the investigators have access to the right AI tools and technologies to tackle these situations.

The post ARTIFICIAL INTELLIGENCE IS PLAYING A BIG ROLE IN FRAUD INVESTIGATION appeared first on Artificial Intelligence.

While many industries are struggling amid the coronavirus pandemic, both the IT industry and the broader trend of transition to remote work have revealed many areas where traditional approaches to managing businesses create unnecessary waste. Still, data science and its subdivision – machine learning – reveal that such expansion is nearly limitless.

Machine learning uses powerful algorithms to discover insights based on real-world data that can then be used to make predictions about future outcomes. As new data comes available, machine learning programs can automatically adapt and produce updated predictions. As with any tool, machine learning is not a silver bullet. However, there are many situations in which the technology can outperform linear and statistical algorithms.

Here are five of the most common use cases where machine learning can make a big difference:

When engineers can’t code rules for certain problems

Many human-oriented tasks (such as recognising whether an email is spam) aren’t solvable using simple (deterministic), rule-based solutions. Because so many factors may influence an answer, engineers would have to write and frequently update billions of lines of code. In addition, when rules depend on too many factors, and when those rules overlap or need fine-tuning, it becomes difficult for humans to code precise rules. Fortunately, machine learning programs don’t require users to encode actual patterns. These programs only need proper algorithms to extract patterns automatically.

When you need to scale a solution to millions of cases

You might be able to manually categorise a few hundred payments as either fraudulent or not. However, this becomes tedious or impossible when dealing with millions of transactions. As user bases grow, it’s no longer feasible for organisations to process payments by hand – end-users today want answers about their money in milliseconds, not minutes or hours. Machine learning solutions are effective at handling these types of large-scale problems with little or no human intervention.

When you can do it manually, but it’s not cost-efficient

There are situations in which in-house experts could process many requests quickly and accurately but at a high cost. For instance, imagine you assess DMV forms for in-state and cross-state car purchases to determine their validity before passing them on. In this situation, the business processes are well-defined, optimised, and serialised. It may take only a few minutes to check each form thoroughly. But allocating so much manual labor to this work is likely not the best use for your budget. Machine learning, on the other hand, offers predictable, pay-as-you-go pricing for fully scaled operations.

When you have a massive dataset without obvious patterns

Consider this – you’ve successfully prepared a well-curated dataset and know the underlying problem. However, you don’t see any explicit patterns in the data, preventing you from encoding those validations. Plus, there are many typos, missing fields, and other human-caused errors with no validation in place. You may even know the data is poor quality and can manually determine every affected row. But you can’t see any actual connections between valid and invalid records. Machine Learning algorithms can solve this problem. They can find hidden connections between data points that aren’t clear to humans. Tools like Interpreting Tracers can even describe how machine learning models arrive at their conclusion.

When you live in an ever-changing universe (adaptive)

The world, and its problems, are always changing. A problem you solved yesterday can easily mutate into something else entirely, rendering your previous solution inefficient or even useless. For example, if your organisation processed medical appointment recordings to extract diagnoses, procedure information, and billing codes, your rules might have to evolve constantly. However, you can’t make updates in real-time 24/7. Meanwhile, incorrectly labelled items could lead to insurance rejections, huge fines, and legal penalties. One major advantage of machine learning methods is that they can learn from data across the entire lifecycle of your application – from the first line of code written to the moment when the model is finally shut down. Moreover, it’s important for production-grade systems to have feedback loops so that you can catch the moment when your model no longer solves problems correctly.

It’s important to remember that machine learning is a tool – it’s not magic. Machine learning models are essentially advanced math-based algorithms, which identify patterns in data and learn from them. However, when properly applied to the right use cases, machine learning can reduce the amount of time spent error-prone manual IT operations, adding significant business value and greatly reducing IT costs.

The post Five common use cases where machine learning can make a big difference appeared first on Artificial Intelligence.

It’s all about the data — or, so they say. If you’ve kept your ear to the ground on business technology, you’ve certainly heard the term Big Data. It’s commonly used to describe massive amounts of unstructured information coming from various channels of our business and personal lives. But what’s so special about Big Data? Nothing. In fact, Big Data alone is about as useful as the internet was in 1984.

Our expectation of instant information gratification in 2021 is high. Our world is so hyperconnected, we are conditioned to needing our data now. Most people don’t realize the enormous network and computer infrastructure required to make data fast. When we think about this in our personal lives, there are many examples: Facebook, LinkedIn, Google and Netflix all have massive amounts of data that need to be delivered to their end users. Search, video, messages — you name it, it’s almost instantaneous.

In the business world, the power behind the curtains of massive data companies is usually not attainable. Thus, for many companies, data may seem slower to reach users at work than it does in daily life. However, businesses are quickly learning the scalable and affordable options they have to make their data fast. Leveraging networking techniques that minimize latency between users and data is key. This includes everything from increasing bandwidth to user locations, keeping users on the same network or keeping frequently used data/content cached for quick access. Modern data storage and computer systems provide much higher levels of performance than conventional systems. Both cloud-based systems and local infrastructure have several options to consider that are flexible and economical for a dynamic business.

Reliable Data

At the end of the day, the data must be available when and where we need it. If we cannot rely on it consistently, then it becomes a risk to the business. Organizations that need reliable data build reliable infrastructure, deploy reliable applications and create a reliable network. Reliability requires an investment in the right resources, but that doesn’t mean it can’t be an affordable endeavor.

Over the past decade, IT costs may have grown but value is on the rise as well. As Digital Transformation shapes the strategy of many organizations, companies are realizing smart technology investments can have a significant ROI. The cost of “downtime” calculation is key to estimating true cost and risk in today’s business. Gartner estimates the average enterprise impact of unplanned downtime to cost $5,600 per minute. Compare that to the falling costs of network, infrastructure and cloud services, and you’ll discover building reliability is worth it.

Usable Data

59 zettabytes. That’s what experts at the IDC claim the global data footprint looks like in 2020. That’s 59 billion terabytes! The IDC also cites that our global consumption of data over the next three years will surpass all of what we’ve produced and consumed over the last 30. That is a staggering statistic that most people cannot comprehend. 

Interestingly, most of this massive data is just not usable to business, science and government. Our ability to mine this data into meaningful information has not kept pace with the data explosion of the last decade. 

One reason for this is that many companies have not invested the time and resources to fully understand the data available to them. The shift to a culture centered around data-first decisions is not easy. Through Digital Transformation initiatives, companies are just starting to tap into the “gold” available to them under the mounds of data that exist. The ability to better understand our business with solid insights is within reach. It will take time, but it will be worth the wait and open doors we can’t even predict today.

Secure Data

For many businesses, the security of data may be the most important on this list, and for good reason. Data is power. For that reason, it is sought after by cybercriminals and leveraged to cripple an organization’s operation by holding data hostage. Make no mistake, we all suffer each time a breach occurs. 

The constant need to be vigilant in security efforts makes it harder than ever to run a successful business. But, with such a complex security arena, many businesses of all sizes struggle to find the right path. If your company wants to improve its security posture, it’s good to consider two concepts.

First, start with a foundation. There are numerous programs and frameworks to help here. The NIST Cybersecurity Framework is an excellent resource to get started. Get educated and identify your baseline so that you can begin to fill company gaps.

Secondly, work in layers. Cybersecurity is best addressed with a layered approach that includes the network, endpoints, email and employee training. Developing a solid plan to minimally cover these four critical areas is an excellent start to securing your data.

Accurate Data

Admit it: You’ve looked at your weekly business reports and said, “That can’t be right.” There is always skepticism in data since we all know there are many ways its accuracy can fall short. Input methods, system processes, bad code and many other issues can invalidate data and leave a business shortsighted on decisions.

To avoid the bad data conundrum, ensure your sources are clean and offer options to keep things in check. Data entry validation, data scrubbing algorithms and enterprise system integration points are excellent approaches to data hygiene. If your business shares data with third parties, do your homework and be sure it’s from a trusted source. That will help you avoid regrettable business decisions based on bad data.  

Big to Better to Best data is what we should expect going forward. In our not-so-distant future, if the data isn’t “best,” it’s just “big.” Be sure to keep pace by addressing some of the techniques mentioned here and demand the most of your data.

The post Five Big Compliments To Big Data appeared first on Artificial Intelligence.

Latest released the research study on Global Cloud Microservices Market, offers a detailed overview of the factors influencing the global business scope. Cloud Microservices Market research report shows the latest market insights, current situation analysis with upcoming trends and breakdown of the products and services. The report provides key statistics on the market status, size, share, growth factors of the Cloud Microservices. The study covers emerging player’s data, including: competitive landscape, sales, revenue and global market share of top manufacturers are AWS (United States),CA Technologies (United States),Contino (United Kingdom),CoScale (Belgium),IBM (United States),Idexcel (United States),Infosys (India),Kontena (Finland),Macaw Software (United States),Marlabs (United States),Microsoft (United States),Netifi (United States),NGINX (United States),OpenLegacy (United States),Oracle (United States),Pivotal Software (United States).

Definition:
A cloud microservices is known as a software development technique and a collection of services which are loosely coupled. Cloud Microservices is a method of developing software systems which tries to emphasis on developing single-function modules with the well-defined operations and interfaces. It is also known as the microservice architecture. Cloud microservices are gaining popularity among companies that need greater scalability and agility. In businesses across the industries, i.e., from telecommunications and retail to the financial services and manufacturing, IT teams are choosing cloud microservices, to develop new applications and break down monoliths

Keep yourself up-to-date with latest market trends and changing dynamics due to COVID Impact and Economic Slowdown globally. Maintain a competitive edge by sizing up with available business opportunity in Cloud Microservices Market various segments and emerging territory.

Market Trend:
Digital transformations

Market Drivers:
Proliferation of the microservices architecture
Customer-oriented business

Restraints:
Security and compliance

The Global Cloud Microservices Market segments and Market Data Break Down are illuminated below:
by Type (Stateless, Persistence, Aggregator), Industry (BFSI, IT and ITeS, Telecommunications, Government, Healthcare, Retail and eCommerce, Media and Entertainment, Transportation and Logistics, Manufacturing, Others (Education, Energy & Utilities, and Travel & Hospitality)), Services (Consulting services, Integration services, Training, support and maintained services), Organisation (SMEs, Large Enterprises), Deployment model (Public Cloud, Private Cloud, Hybrid Cloud), Component (Platform, Services)

Analyst at AMA have conducted special survey and have connected with opinion leaders and Industry experts from various region to minutely understand impact on growth as well as local reforms to fight the situation. A special chapter in the study presents Impact Analysis of COVID-19 on Global Cloud Microservices Market along with tables and graphs related to various country and segments showcasing impact on growth trends.

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa
Country Level Break-Up: United States, Canada, Mexico, Brazil, Argentina, Colombia, Chile, South Africa, Nigeria, Tunisia, Morocco, Germany, United Kingdom (UK), the Netherlands, Spain, Italy, Belgium, Austria, Turkey, Russia, France, Poland, Israel, United Arab Emirates, Qatar, Saudi Arabia, China, Japan, Taiwan, South Korea, Singapore, India, Australia and New Zealand etc.

Strategic Points Covered in Table of Content of Global Cloud Microservices Market:
Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Cloud Microservices market
Chapter 2: Exclusive Summary – the basic information of the Cloud Microservices Market.
Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of the Cloud Microservices
Chapter 4: Presenting the Cloud Microservices Market Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.
Chapter 5: Displaying market size by Type, End User and Region 2015-2020
Chapter 6: Evaluating the leading manufacturers of the Cloud Microservices market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
Chapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries (2021-2026).
Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

Finally, Cloud Microservices Market is a valuable source of guidance for individuals and companies in decision framework.

Data Sources & Methodology

The primary sources involves the industry experts from the Global Cloud Microservices Market including the management organizations, processing organizations, analytics service providers of the industry’s value chain. All primary sources were interviewed to gather and authenticate qualitative & quantitative information and determine the future prospects.

In the extensive primary research process undertaken for this study, the primary sources – Postal Surveys, telephone, Online & Face-to-Face Survey were considered to obtain and verify both qualitative and quantitative aspects of this research study. When it comes to secondary sources Company’s Annual reports, press Releases, Websites, Investor Presentation, Conference Call transcripts, Webinar, Journals, Regulators, National Customs and Industry Associations were given primary weight-age.

What benefits does AMA research study is going to provide?
• Latest industry influencing trends and development scenario
• Open up New Markets
• To Seize powerful market opportunities
• Key decision in planning and to further expand market share
• Identify Key Business Segments, Market proposition & Gap Analysis
• Assisting in allocating marketing investments

Definitively, this report will give you an unmistakable perspective on every single reality of the market without a need to allude to some other research report or an information source. Our report will give all of you the realities about the past, present, and eventual fate of the concerned Market.

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Southeast Asia.

Craig Francis (PR & Marketing Manager)
AMA Research & Media LLP
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Advance Market Analytics is Global leaders of Market Research Industry provides the quantified B2B research to Fortune 500 companies on high growth emerging opportunities which will impact more than 80% of worldwide companies’ revenues.
Our Analyst is tracking high growth study with detailed statistical and in-depth analysis of market trends & dynamics that provide a complete overview of the industry. We follow an extensive research methodology coupled with critical insights related industry factors and market forces to generate the best value for our clients. We Provides reliable primary and secondary data sources, our analysts and consultants derive informative and usable data suited for our clients business needs. The research study enables clients to meet varied market objectives a from global footprint expansion to supply chain optimization and from competitor profiling to M&As.

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