No wonder, Artificial Intelligence is the future. We’ve seen its application in possibly every field now. The problem isn’t with the technology, it is with the biasness that goes in, says Timnit Gebru. She goes on to add that it is built in a manner that replicates the white work force that’s mostly men-dominated making it. Right from her first lecture in Spain which is by far the world’s most important conference on AI till date, she has seen a vast difference in the number of men and women, obviously men being dominant in number. She highlights two things that she believes remained constant over a period – one, how technologically advance we’re becoming with every passing day and two, how bias the work culture is but the companies fail to acknowledge.

Later, Dr. Gebru co-founded an organization, Black in AI, a community of black researchers working in artificial intelligence. She completed her Ph.D. and was then hired by Google. It was during this time that she told Bloomberg News how AI suffers from what she called “sea of dudes” problem. This left everyone stunned. She talked about how she worked with hundreds of men over a period of 5 years and the number of women could be counted on fingers.

It just doesn’t end here. A few years back, a New York researcher saw how biased AI was against Black people. An incident wherein a Black researcher learned that an AI system couldn’t identify her face till she had put up a white mask raised eyebrows.

Amidst all this, Dr. Gebru was fired. She said that this was an aftermath of her criticism against Google’s minority hiring. When Dr. Mitchell defended, Google removed her too without leaving any comments. Now, this sparked arguments among the researchers and tech workers.

Things got worse when image recognition was what Google tried its hands on. The AI model was trained to categorize the photos on what was pictured – for example dogs, birthday party, food, etc. But, this is when one user saw a folder named “Gorillas”. On opening the same, he found about 80 photos that he had clicked with a friend during a concert. His friend was black. The point of discussion is that this AI model is trained by engineers who choose data.

Yet another case on the same lines is that of Deborah Raji, a black woman from Ottawa. She worked for a start-up and once she saw a page filled with faces. The company uses theses faces to train its facial recognition software. She kept scrolling only to find more than 80% images were of white people and more than 70% of those were men. She was working on a tool that’d automatically identify and remove pornography from images people posted to social networks. The system was meant to learn the difference between the pornographic and the anodyne. This is where problems stepped in. The G‑rated images were dominated by white people but pornography was not. This is why the system was beginning to identify Black people as pornographic. This is why choosing the right data matters and since the ones who chose this data were mostly white men, they didn’t find anything wrong with this.

Before working for Google, Dr. Gebru joined hands with Joy Buolamwini, a computer scientist at the MIT. Ms. Buolamwini, who is Black, too faced biasness when she was working. She narrated her experience quite a few times when an AI system recognized her face only when she wore a white mask.

During the later years, Joy Buolamwini and Deborah Raji joined hands to test the facial recognition technology from Amazon. It marketed its technology under the name Amazon Rekognition. They found that Amazon’s technology too faced difficulties while identifying the sex of female and darker-​skinned faces. Later, Amazon called for government regulation of facial recognition. The company did not step back from attacking the researchers both in private emails and public blog posts.

Later, Dr. Mitchell and Dr. Gebru came up with an open letter wherein they rejected Amazon’s argument and called on it to stop selling to law enforcement.

Dr. Gebru and Dr. Mitchell had struggled a lot to bring the change in the organizations that they were working with. But, that didn’t pay off.

Dr. Gebru came up with a research paper that she wrote with six other researchers, including Dr. Mitchell. The paper talks about a system built by Google that supports its search engine and how it can show bias against women and people of colour.

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AI in healthcare is growing multifold, from diagnostics to pain management

Artificial Intelligence has been playing a growing role in the world in the last few decades. What most don’t understand is artificial intelligence introduces itself in numerous structures that sway everyday life. Signing into your social media, email, car ride services, and online shopping platforms, etc. all include artificial intelligence algorithms to improve customer experience. AI in healthcare is growing quickly; explicitly, in diagnostics and treatment management.

As of late, AI applications in healthcare have sent huge waves across medical services, fuelling a conversation of whether AI doctors will in the end supplant human doctors in the future. However, experts believe that human doctors won’t be supplanted by machines soon, yet the use of artificial intelligence in healthcare can help doctors to settle on better clinical choices or even supplant human judgment in certain functional areas of healthcare.

One emerging application of AI in healthcare is a new study led by the research team of Northwestern University faculty and alumni, created and applied artificial intelligence, or machine learning algorithms to physiological information — including respiratory rate, oxygen levels, pulse rate, body temperature, blood pressure, etc. from patients with chronic pain suffering sickle cell illness. Not exclusively did the scientists’ methodology beat baseline models to gauge subjective pain levels, it additionally distinguished changes in pain and abnormal pain fluctuations.

The team of researchers utilized data from 46 adults and kids with sickle cell sickness over a consolidated total of 105 hospital stays, taking a look at the physiological information alongside patient-reported pain scores to create models that could derive pain levels and identify changes in pain level through machine learning

Presently, patients should evaluate their pain on a scale of zero to 10. This can be a troublesome assignment on the grounds that numerous individuals experience pain in an unexpected way, and little kids and unconscious patients can’t rate their pain by any means. The scientists say that these subjective evaluations of pain could be enhanced with a more objective, less intrusive, data-driven approach to help physicians with a more accurate treatment plan.

The researchers then analyzed their new models against existing ones that attempt to evaluate levels of pain but that don’t use physiological estimations. The new models outflanked the current ones.

According to Daniel Abrams at Northwestern University in Illinois, “The big picture is that we want to better understand how people experience pain. We’re hoping that the long-term outcome of this line of research is a more quantitative approach to pain management.”

Prior to this use of AI in pain management, Professor Jeff Hughes, Chief Scientific Officer at PainChek, clarifies how smart automation and AI in healthcare can reform pain assessment in patients with dementia who find it very difficult to communicate.

PainChek was created as a compelling solution for this issue. Its novel blend of automated facial-analysis technology and smart automation empowers care takers and healthcare experts to look for the presence of pain when it isn’t self-evident, to evaluate the intensity of pain and screen the effect of treatment to optimize and witness the overall quality of care.

This information is then integrated with non-facial features seen by the application user and information through a range of digital checklists, which together permits automatic calculation of a total pain score and the task of a pain intensity level.

There are different impacts of AI in healthcare. Commonly, AI used in healthcare leverages a web data set permitting doctors and experts to access a lot of diagnostic resources. As doctors are profoundly knowledgeable in their field and are up-to-date with present research, AI technology in healthcare incredibly builds a quicker result that can be matched with their clinical knowledge.

Nonetheless, artificial intelligence in healthcare presents numerous apprehensions as discussed, particularly in the clinical setting, of in the long-run substituting or lowering down the requirement for human doctors. In any case, so much research and data have shown that it is almost certain that AI in healthcare will benefit and improve clinical diagnostics and decision making as opposed to lessen clinician need.

The opportunities for AI applications in healthcare from emergency clinics and primary care, to home care, are huge. The use of artificial intelligence in healthcare can automate patient assessment and eliminate assessor bias. It can assess patient risk, for example, of a patient building up a specific disease, analyze illness, for instance, by deciphering ECG results and X-ray pictures, select the ideal treatment dependent on a patient’s clinical history and the results of clinical trials, and monitor disease and recognize early warning signs of deterioration.

The use of AI in healthcare will be driven by the availability of big data on which to train predictive algorithms, which help (instead of supplanting) human doctors, encourage curiosity-based reasoning, empower collaboration and eliminate unremarkable tasks, thus, improving patient care.

The post AI IN HEALTHCARE: AI IN PAIN MANAGEMENT, A NEW APPLICATION appeared first on Artificial Intelligence.

The world right now runs on a network of trillions of signals sent from billions of computer applications designed and maintained by thousands of people. It is hence safe to assume that life right now runs digitally.

It was not the same a couple of decades ago but. The people who had computers were the exception and now it is the opposite.

All these software, technologies, and applications are a result of brainstorming by many technology enthusiasts who constantly work to make human life simpler.

That being said, it is known that whenever the outcome is a machine that simplifies human life, the technology behind it is as complicated. Android app development services could be a great example of this as they run on thousands of different network nodes and create applications to run on mobile phones.

A Glance At Cloud-Native Application Development

The latest revolution in this field is cloud-native application development. But what is it and how has it become so important for developers in the technological sector? A series of tiny, autonomous, and loosely coupled services are cloud-native applications.

They are intended to have well-recognized market benefits such as the ability to integrate customer input for quality improvement quickly.

The creation of cloud-native applications is an approach to designing, running, and enhancing apps based on well-known cloud computing techniques and technologies. An IoT app development company chooses cloud-native apps because they are easy to build, have a faster process, and are also highly scalable. 

If an app is “cloud-native,” it is designed specifically to provide a seamless experience of creation and automated management through private, public, and hybrid clouds.

Hence, if Android app development services can build new applications faster, optimize existing ones, and connect them all through cloud-native computing, they will be delivering applications more rapidly like how the business demands in competitive times. But for this formula to work, the applications must be programmed using the right language since that is what guarantees the quality of the applications to be top-notch.

While there are so many programming languages out there, Java and Python are the most preferred for cloud-native apps because of the reasons listed below.

Java For Cloud Computing

Java has been in business for way too long to now suddenly be labeled obsolete simply because there are new and more creative languages. Even now Java development services are using Java to develop and maintain applications with cutting-edge technology due to its robustness, security enhancement, ease of use, and the ability to transfer to multiple platforms.

The reason for developers and businesses to choose Java-powered cloud-native application development was to build custom apps faster without compromising on the standards of quality required to sustain in the competitive market. It has been used to create Gmail, Hadoop platform, Confluence, etc.

Java as a programming language only adds to this goal. Java as a programming language is secure, portable, and stable and also ensures high-performance execution without consuming unnecessary time.

Java offers the powerful framework required to support the multi-cloud store, cloud computing, and reactive programming for updating and improving applications. A Java development company backs Java as the preferred language for the following reasons:

• Serverless architecture can be supported by Java.
• AOT (ahead-of-time) compilation and microframeworks are possible with Java.
• Big size distribution is also possible because of the flexibility of Java.
• A Java development company can also access reusable codes and is product-oriented to create custom applications.

Python For Cloud-Native Application Development

Python simplifies the production of web applications, APIs, academic programming, and data science. Python is regarded as an attractive programming language that supports growth opportunities in diversified fields.

Python is one of the few languages that can be used for manipulating and processing massive data sets that are highly suitable. Python is most suitable for cloud computing for neural networks, machine learning, and streaming analytics systems.

Features like the ease of learning, brisk and easy to use data structures, third-party modules, far-reaching support libraries, community development, and efficient production of applications make Python the first choice of every IoT development app company.

Python is also called the preferred language due to the successful applications it has already created. The most trending apps like Netflix, Pinterest, Reddit, Spotify, and Instagram have all been created using Python.

With a portfolio like this one to testify its efficiency, it is safe to assume that Python even after thirty years of being in existence has managed to keep up with the changing rules of application development and has also justified its place at the top by programming applications that are used worldwide by billions of people. A few more reasons to choose Python as a programming language are listed below:

• Python can be used to build all kinds of apps such as business applications, image and design applications, GUI-based desktop applications, scientific and computational applications.
• Python is efficient when cloud computing involves neural networking.
• It is easy to use while streaming analytics structures.
• Ease of integration for hybrid applications running on several operating systems.

Few Final Words

When it comes to cloud programming, in order to get better goods, it is important to use data-oriented languages rather than general-purpose ones. With the amount of technological development happening round the clock and the competition taking place on a global level, it has become very tough for companies to create applications that are not only unique but also efficient.

There is also the need to be the first one in innovation and development to survive in the ever-evolving tech industry. Ever since cloud computing has begun, building apps has become somewhat easier due to its speed.

However, building efficient and bug-free apps means using a robust programming language that does not compromise on the scalability and innovation of the app. Python and Java development services have proven to be two of the most preferred languages for cloud-native application development as they are easy to use, highly portable, and efficient.

The post Why Are Java And Python The Most Preferred For Cloud-Native Application Development? appeared first on Artificial Intelligence.

A machine learning application’s value is dependent on the quality of data used to train and deploy it. Organizations are responsible for creating or acquiring enough data, that this data is useful for the specific application and that the analytics team is capable of sorting through and learning useful things from it.

The knowledge discovery in databases (KDD) finds knowledge in data; organizations use data mining methods to draw out its usefulness.

KDD vs. data mining

While most data scientists are familiar with data mining, KDD is a specialized process that applies high-level, sophisticated data mining techniques to find and interpret patterns from data. Though the terms are sometimes used interchangeably, KDD is used especially for machine learning, databases, pattern matching, AI and enterprise use.

“[In comparison], the term data mining is broadly applied to looking through piles of data and trying to find interesting patterns,” said Peter Aiken, associate professor at Virginia Commonwealth University.

In general, these processes both extract data from large databases, but KDD is more often used to explain the larger picture. There are varying divisions of the steps of KDD but in general they can be broken down into several steps:

Step 1: Selection — Sort out the data you would like to mine.

Step 2: Preprocessing — Data cleaning (removing any noise or outliers within the data set) using statistical techniques or data mining algorithms.

Step 3: Transformation — Data is prepared and developed through dimension reduction and attribute transformation. This step may be quite project-specific but always crucial to the success of the project.

Step 4: Data mining — Outline what kind of data mining would be most useful by judging which objective you are seeking (prediction or description).

Step 5: Interpretation/Evaluation — Assess and interpret the mined patterns, rules, and reliability in comparison to the original objective.

Association rules

Data mining is the process of identifying patterns and establishing relationships by sorting through data sets. Within this broad definition are association rules that analyze the data set for if/then patterns and use support and confidence criteria to locate the most important relationships. Support is how often items appear in the database and confidence is the amount of if/then statements that are correct.

Among the more common data mining parameters include anything from sequence analysis, classification and clustering, as well as forecasting.

Sequence analysis. Identifies patterns where one event points to another, later event.

Classification. Looks for new patterns and can change the way in which the data is organized.

Clustering. Locate and document groups of facts that had not been known yet. Groups are organized by how similar they are to one another.

Forecasting. These parameters within data mining discover patterns in data that point to reasonable predictions.

This is all a relatively manual process, however. Human intervention and decision-making come to play majorly in the KDD/data mining process. This is one of the largest differentiators from a similar process, machine learning. When it comes to machine learning, the quality of data is crucial and data mining allows for better insight to be drawn out from this data.

“Usually the most critical thing in [removing deficiencies in] performance of your model is also usually the most critical step in getting your model put into production,” said Kjell Carlsson, a Forrester Research analyst.

KDD, data mining and machine learning

If an enterprise is working on a machine learning project, then some form of the KDD process is also going on in-house. Both fall under the umbrella of data science and both processes are used for solving complex problems with data.

“The real question is from a user’s perspective, what are you trying to do,” Aiken said. “And if the data that you’re trying to use is more likely to come from a database than a big data pile.”

Machine learning and data mining share the same principles but function differently. A data scientist turns to data mining to pull from existing information to find emerging patterns that can help shape decision-making processes. Machine learning is more active and less hands-on. Machine learning takes this process a step further because it can learn from the existing data and teach itself what to look for in the future and predict patterns. Data mining is typically used as an information source from which a machine learning algorithm can learn.

Both are analytics processes that are good with pattern recognition and are therefore often confused. Machine learning may use some data mining techniques to build its models and data mining can use machine learning techniques to produce more accurate analysis.

“The biggest problem with computer science in today’s environment is that machine learning algorithms don’t have training data,” Aiken said.

Without training data, a machine learning model is unable to reach any kind of effective performance. As Aiken sees it, any boasting about a model without data is like saying well you’ve got this great baseball team you just have to teach them how to play baseball.

Uses of KDD/data mining and machine learning

Data mining and the overall process of KDD have carved out their own specialty. Data mining has been deployed in the retail industry in order to better understand the patterns of customer buying habits. Organizations can mine their customer data for relevant information on the success and failure of items and adjust from there.

It has also been used in finance by organizations looking into potential investments and whether a new organization is going to succeed. Past performance of successful startups, as well as patterns of indicators of business prowess, inform those in the finance industry of where to put their money.

Machine learning’s applications vary widely across industries for purposes such as fraud detection, autonomous vehicles and personalized marketing, among others. Organizations turn to machine learning algorithms to analyze vast amounts of data and provide continued growth and value as more data is brought in.

Machine learning algorithms can function better with relevant data sets and these can be brought about through the process of data mining.

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Ludré Stevens is Chief Product Officer at Inbotiqa, a next-generation Intelligent Business Email for high-volume and group mailboxes utilising the power of AI. Here he shares how artificial intelligence can be used in the workplace during Covid-19.

The onset of the Covid-19 pandemic and the dramatic changes in working practices it necessitated have created many challenges, for organisations, teams and individual staff members. Entire workforces suddenly shifting to working remotely has meant banks needing to support thousands of employees as they work from home, keep track of work execution and maintain regulatory compliance. All this while also needing to ensure high standards of customer service and SLAs are met.

With a return to widespread normal office working looking unlikely, effective long-term solutions to elevated compliance risks and other challenges need to be adopted and embedded. Video conferencing and collaboration tools, artificial intelligence (AI)-driven automated solutions, analytics, and more, have helped operations to continue, recording and monitoring issues to be addressed, and have even improved productivity and management insights.

The vast amounts of data involved in the financial services industry are beyond human scale so the application of AI and machine learning (ML) in order to meet regulatory requirements, boost productivity and cut costs was already prevalent. The pandemic and the changes it has wrought have only increased the need for and accelerated the adoption of AI and ML solutions.

For example, AI and ML can be used for surveillance and behaviour tracking to find issues, meaning specialised AI and ML, in-house built or vendor-built tools, are being employed to support the new normal. Employee-surveillance software that utilises the likes of ML and behavioural rules engines was already being used by enterprises inside their office spaces.

Since lockdown started, there has been an increase in the use of such products to monitor remote-working practices. The use of surveillance tools when employees are working remotely throws up its own privacy issues, though, if they are deemed too intrusive.

A recent Bain Consulting report found that three out of four companies planned on accelerating automation initiatives across the board post-Covid, including those dependent on AI/ML, with the financial services industry having the largest percentage of respondents with this on their roadmap, at 93%. This despite the fact that many technology executives were already expressing uncertainty about their investments in machine learning and dissatisfaction with the way their companies were adopting it.

Dedicated AI-driven Regtech companies such as providers of behavioural analytics and big data and analytics or AI-powered biometrics that bolster document verification for anti-money laundering (AML) and know-your-customer (KYC) capabilities, were already established in the market place.

Companies often opt to buy rather than build solutions due to cost, time and talent issues, and there is an increasing choice of third-party tools on the market. Technologies already being built or bought to boost productivity and aid compliance have also proved uniquely helpful in facilitating remote working during this swift adjustment.

The flexibility of tools is also an important consideration. For example, our YUDOmail intelligent business email system uses its own ML to classify communications such as emails to route and classify work, meaning work can be allocated and tracked to people working remotely.

However, in addition, it also allows for vendor AI and ML tools as our structured email data can be easily ingested by them.

This structured email data including related metadata (audit trail and threads) saves other AI tools from having to search for email data and then structure it themselves. By also providing a delivery method for any workflow outcomes from these AI tools, the loop is closed.

When it comes to auditing, the man-hours alone spent on gathering data for audit purposes can be considerable, so it makes sense to integrate a compliance-driven recording strategy into communications so that an audit trail is created in real-time. Supporting asynchronous working, where employees are on more flexible hours now working from home, is another aspect of the new normal of increased remote working that needs to be considered and addressed and where AI can provide value.

Whatever the specific solutions opted for are, and whether employees are working from home or in the office, it’s clear that AI and ML are playing a big and rapidly increasing role in the financial services industry. The Bain Consulting report also notes that what separates the leaders from the pack is their ability to make real changes in the way they get work done and integrate AI into products and processes across the organisation, allowing them to differentiate themselves based on AI-driven insights.

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It’s not unusual for developers and architects who jump into microservices for the first time to “want it all” in terms of performance, uptime and resiliency. After all, these are the goals that drive a software team’s decision to pursue this type of architecture design. The unfortunate truth is that trying to create an application that perfectly embodies all of these traits will eventually steer them to failure.

This phenomenon is summed up in something called the CAP theorem, which states that a distributed system can deliver only two of the three overarching goals of microservices design: consistency, availability and partition tolerance. According to CAP, not only is it impossible to “have it all” — you may even struggle to deliver more than one of these qualities at a time.

When it comes to microservices, the CAP theorem seems to pose an unsolvable problem. Which of these three things can you afford to trade away? However, the essential point is that you don’t have a choice. You’ll have to face that fact when it comes to your design stage, and you’ll need to think carefully about the type of application you’re building, as well as its most essential needs.

In this article, we’ll review the basics of how the CAP theorem applies to microservices, and then examine the concepts and guidelines you can follow when it’s time to make a decision.

CAP theory and microservices

Let’s start by reviewing the three qualities CAP specifically refers to:

• Consistency means that all clients see the same data at the same time, no matter the path of their request. This is critical for applications that do frequent updates.
• Availability means that all functioning application components will return a valid response, even if they are down. This is particularly important if an application’s user population has a low tolerance for outages (such as a retail portal).
• Partition tolerance means that the application will operate even during a network failure that results in lost or delayed messages between services. This comes into play for applications that integrate with a large number of distributed, independent components.

Databases often sit at the center of the CAP problem. Microservices often rely on NoSQL databases, since they’re designed to scale horizontally and support distributed application processes. And, partition tolerance is a “must have” in these types of systems because they are so sensitive to failure.

You can certainly design these kinds of databases for consistency and partition tolerance, or even for availability and partitioning. But designing for consistency and availability just isn’t an option.

The PACELC theorem

This prohibitive requirement for partition-tolerance in distributed systems gave rise to what is known as the PACELC theorem, a sibling to the CAP theorem. The acronym PACELC stands for “if partitioned, then availability and consistency; else, latency and consistency.” In other words: If there is a partition, the distributed system must trade availability for consistency; if not, the choice is between latency and consistency.

Designing your applications specifically to avoid partitioning problems in a distributed system will force you to sacrifice either availability or user experience to retain operational consistency. However, the key term here is “operational” — while latency is a primary concern during normal operations, a failure can quickly make availability the overall priority. So, why not create models for both scenarios?

It may help to frame CAP concepts in both “normal” and “fault” modes, provided that faults in a distributed system are essentially inevitable. This enables you to create two database and microservices implementation models: one that handles normal operation, and another that kicks in during failures. For example, you can design your database to optimize consistency during a partition failure, and then continue to focus on mitigating latency during normal operation.

Applying PACELC to microservices

If we use PACELC rather than “pure CAP” to define databases, we can classify them according to how they make the trades.

• In PACELC terms, relational database management systems and NoSQL databases that implement ACID (atomicity, consistency, isolation, urability) are designed to assure consistency, classifying them as PC/EC. Typical business applications, like human resources apps and ticketing systems, will likely use this model, particularly if there are multiple users using different component instances. Google’s Bigtable database is a good example of this.
• In-memory databases like MongoDB and Hazelcast fit into a PA/EC model, which is best suited for things like e-commerce apps, which need high availability even during network or component failures.
• Real-time applications, such as IoT systems, fit into the PC/EL model that databases like PNUTS provide. This is the case in any application where consistency across replications is critical.
• Database systems based on the PA/EL model, such as Dynamo and Cassandra, are best for real-time applications that don’t experience frequent updates, since consistency will be less of an issue.

Know the tradeoffs

The bottom line is this: It’s critical to know exactly what you’re trading in a PACELC-guided application, and to know which scenarios call for which sacrifice. Here are three things to remember when making your decision:

• Consistency is most valuable where many users update the same data elements.
• Availability is critical for applications involving consumers (who get frustrated easily) and also for some IoT applications.
• Latency is most likely critical for real-time and IoT applications where processing delays must be kept to a minimum.

Make your database choice wisely. Then, design your microservices workflows and framework to ensure you don’t compromise your goals.

The post The CAP theorem, and how it applies to microservices appeared first on Artificial Intelligence.

Imagine a bank whose customers can tap on a wearable device to make a payment and receive updates on investments through AI-generated insights. A bank that enables its customer to own their data through the application of blockchain technology and share it with lenders as a validated credit history when applying for a loan; that bank is likely to operate for the long-term.

According to a PwC global CEO survey, 70 percent of financial services leaders stated that the speed of technological change is one of their biggest concerns.

Clients demand more convenience and customisation from their banks, delivered through technology-driven innovation.

This trend will accelerate as other industries digitise, allowing microservices to be monetised. New technologies are tooling up non-traditional players such as Neobanks, mobile network operators, e-commerce platforms, and supermarkets with the means to tokenise exchange and intermediate supply chains exclusive of traditional banks.

These new entrants in the financial sector are a competitive threat to conventional regulated players. For banks to survive and win in this new paradigm, they will need to adopt technology-driven business models; they must develop an internal culture that is tech-minded, encourage idea generation and execution across departments.

But change doesn’t happen in a vacuum. For impactful transformation, banks will need to involve their clients, employees, and communities to build solutions that meet evolving needs across a broad social spectrum.

Digital transformation can only be optimal through collaboration with the clients, the Fintech community, regulators, service providers, and others applying digital tools to meet their financial goals.

Hence, we see traditional banks partnering with FinTechs, MNOs, and digital marketplaces by design or necessary survival.

These alliances are symbiotic in that the Fintechs see three evolving challenges.

First, as they build new digital business models, their activities are becoming economically significant; hence the central bank, other regulators, and governments are getting concerned about potential downside credit and systemic risks.

Their days of minimal or no regulatory oversight are coming to an end.

Secondly, traditional banks are not sitting back and are beginning to incorporate digital solutions.

Thirdly, at scale, traditional banks still dominate the sovereign, corporate finance, and long-term lending markets.

TALENT SHORTAGE

On the other hand, while banks lack agility and sufficient qualified and digitally literate managers, they have trusted compliance capabilities.

They are accustomed to the regulatory minefield and can help FinTechs navigate that space. Partnerships with banks lend credibility to the ventures they participate, and in so doing, there is a reverse transfer of skills from the banks to FinTechs.

Banks can “stand in the gap” between regulators and FinTechs interpreting the use of new technologies to address regulatory concerns.

For instance, Financial institutions can address authentication of human identity when opening an account through the internet of things (IoT) and artificial intelligence (AI). These technologies use various metrics, including location services, user image movement, facial recognition, and temperature checks, to determine whether the person operating the device is an actual human.

Partnerships can facilitate the resolution of challenges facing various sectors. For example, SMEs require access to finance, markets, and simple digital business efficiency tools.

The community of stakeholders in financial services sectors and other industry verticals enabled by the Third and Fourth Industrial Revolution technologies creates a global data lake that will be a massive enabler of innovation in the financial services sector.

By gaining API access to various data sources, banks can create customised solutions for different clients across the region.

Digital access will minimise direct contact with banks. Soon, data portability, possibly using distributed ledger technology will allow customers to use their data as an asset by being part of a community or use it to receive bespoke services.

The future of finance is commoditised services on a network of data connected to value.

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Machine learning depends on data scientists to handle the ML configurations and data inputs

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 and machine learning are using new tools to 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 doesn’t fit for all. Business organizations want machine learning to do analysis on large data, which is complex and difficult. They neglect the fact that machine learning can’t 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 company’s working system might drag down its improvement. Many products that incorporate machine learning capabilities use predetermined algorithms and many diverse ways to handle data. However, each organization’s 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 company’s 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 not Sorcery

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 to make predictions using that data.

However, machine learning can’t 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 algorithm’s 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.

Machine learning problems

As mentioned earlier, machine learning alone can’t 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.

AutoML helps machine learning out of the chaos

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 guesses to 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.

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BigPanda Inc. and Datadog today announced they will integrate their respective platforms to make it easier to form artificial intelligence for automating IT operations (AIOps) to consume data generated by the Datadog IT monitoring service. The two companies are pledging to invest in a go-to-market partnership to create a comprehensive platform for monitoring, analytics and incident management.

As part of that effort, Datadog is committing to making available an “integration tile” through which BigPanda will also be able to feed root cause analytics based on machine learning algorithms into the Datadog console. That capability will be enabled by a service-to-service topology map created by Datadog that will be used to drive event enrichment, correlation, impact analysis and prioritization.

Mohan Kompella, vice president of product marketing for BigPanda, said Datadog affords his company a unique opportunity to better train AI models using a massive amount of IT and security metrics that Datadog aggregates daily. That data can then be combined with data ingested from other IT service management systems, which will enable IT operations to automate processes using better-trained AI models, added Kompella. Thanks to modern application programming interfaces (APIs), Kompella noted it’s become a lot easier for platforms such as BigPanda to consume a wide range of data.

The more data an AI model is exposed to the faster it learns about the IT environment an organization is seeking to automate. Each IT environment is unique, so it usually takes some time for AI models to be optimally tuned. The more data that’s made available, the faster the AI platform is able to make accurate recommendations. It’s then up to each IT team to decide to what degree they want the AIOps platform to automate a task based on those recommendations versus implementing themselves. The more accurate the recommendations become over time, the more confidence IT teams gain in the AIOps platform.

Of course, IT environments tend to change rapidly and evolve in the DevOps era. The alliance with Datadog will provide BigPanda with a primary source of data from tools that are already widely employed by DevOps teams.

Ultimately, the goal is to reduce the mean time to resolution for IT incidents, in addition to eliminating as much as possible the need to invite developers and IT operations teams to a “war room” meeting to discover the root cause of an issue. Many IT teams these days find themselves spending weeks trying to ascertain the root cause of an issue that, once discovered, only takes a few minutes to fix. AIOps platforms afford IT teams the opportunity to eliminate a lot of friction that currently exists in those complex IT environments that have scaled beyond the ability of humans to manage without some AI assistance.

In the wake of the economic downturn brought on by the COVID-19 pandemic, interest in AIOps is clearly on the rise. IT environments continue to grow in size as the enterprise IT becomes more extended. It won’t be long before most IT teams expect to be able to leverage AI to manage highly distributed computing environments. It may, of course, be a while before IT teams fully trust those platforms; however, it’s also clear IT as a whole is reaching a point where there is no alternative.

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Physical robots have been around for almost a hundred years, but not without their limitations. In the 1990s the idea of the collaborative robot, or cobot, emerged to help find ways to put robots in closer proximity to humans, but it is only through the inclusion of AI that robotics can continue to progress.

While the term robotics conjures up visions of hardware machines performing a wide range of tasks, the term robot is now used to describe any sort of software or hardware-based automation that can perform a task.

However, many of these software robotics systems are limited in their ability and are not able to communicate with other systems or robots to carry out these tasks. With the addition of machine learning, robots and cobots can improve their communication and handle even more complex tasks without the normal risk associated with simpler bots.

Cobots and software

Cobots are physical robots that are intentionally designed to operate in close quarters with humans. They are finding increasing use in a variety of different settings, performing pick-and-pack warehouse activities, delivery of goods, and a variety of assistive roles. Increasingly, we are seeing cobots in places as diverse as retail stores, museums, hotels, hospitals and even inside homes.

In this context, robotic process automation (RPA) refers to those software automations that perform repetitive user interface-based tasks that would otherwise be performed by a human, such as typing, clicking, swiping, copying and pasting, and a range of UI-based interactions.

But, if a form layout changes, or additional fields of information are required, these bots are not able to process and handle these exceptions and changes, causing them to fail and making them very brittle.

How AI and machine learning are working with robotics

What makes a robot powerful is an ability to think on its own. This is where artificial intelligence and robotics can come together. Companies are increasingly looking for robots to move past automation and tackle more complex and high-level tasks.

AI can help a robot do a lot of tasks, from successfully navigating their surroundings, to identifying objects around the robot or assisting humans with various tasks such as bricklaying, installing drywall or robotic-assisted surgeries.

Robots can benefit from AI and machine learning in different ways, and these AI-enabled capabilities include:

Computer vision. AI and computer vision technologies can help robots to identify and recognize objects they encounter, help pick out details in objects and help with navigation and avoidance.

AI-enabled manipulation and grasping. Long considered a difficult task for robots, AI is being used to help robots with grasping items. With the help of AI, a robot can reach out and grasp an object without the need for a human controller.

AI-enhanced navigation and motion control. Through enhanced machine learning capabilities, robots gain increased autonomy, reducing the need for humans to plan and manage navigation paths and process flows. Machine learning and AI help a robot analyze its surroundings and help guide its movement, which enables the robot to avoid obstacles, or in the case of software processes, automatically maneuver around process exceptions or flow bottlenecks.

Real-world perception and natural language processing. For robots to have some level of autonomy, they often need to be able to understand the world around them. That understanding comes from AI-enabled recognition and natural language processing. Machine learning has shown significant ability to help machines understand data and identify patterns so that it can act as needed.

In the past, researchers have long thought about how to apply artificial intelligence to robotics but ran into limitations of computational power, data constraints and funding. Many of those limitations are no longer in place, and as such, we now may be entering a golden age of robotics. With the help of machine learning, robots are becoming more responsive, more collaborative, and integrated into other systems.

Likewise, many of the RPA vendors are adding intelligent process automation to their bots to help increase their usefulness. As such, they are looking at AI technologies such as NLP or computer vision to help make these bots more intelligent. Bots that leverage machine learning and adapt to new information and data can be considered intelligent tools that can significantly impact and increase the tasks performed rather than just bots.

Growth of robotics

The use of robots in many industries is becoming increasingly common. These robots can either be physical robots or software bots. It is estimated that there will be 3 million industrial robots in operation during 2020. Furthermore, Gartner projected that RPA software spending was over $1.3 billion in 2019. As such, the need and desire for bots of all sorts is seemingly only to going to increase.

Examples of AI powered robotics include: robotic surgery tools that are able to assist surgeons, law enforcement bomb robots that are able to navigate into dangerous terrain to minimize human injury and casualty, and food and package sorting robots that are able to sense different materials and properly pick and sort the objects.

With the use cases seemingly limitless and cutting across many sectors, there is much innovation still to be had and the robotics industry isn’t going away anytime soon. Many companies are finding increasing value, efficiency and accuracy from bringing robots into their various operations. This stems from the proof of ROI in the industry and, as people continue to feel more comfortable working with robots, companies will continue to invest in the technology. The addition of artificial intelligence into robotics its making them more useful than ever before.

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