Key Archives - Artificial Intelligence

Key Ingredients to Power up Artificial Intelligence in 2021

aiuniverse — Wed, 23 Jun 2021 11:00:09 +0000

Source – https://www.eletimes.com/

Almost everyone in this world is utilising the power of Artificial Intelligence for efficient service in this fast-paced busy life. It is well-known that the combination of the digital transformation and the tech-driven era is contributing to the growth of AI and machine learning models. Reputed organisations and start-ups are motivated to adopt the power of Artificial Intelligence to boost productivity as well as ease the workflow. Ever wondered about what are the factors steering the widespread recognition of Artificial Intelligence? Let’s dig into the five key factors to understand the reasons behind this revolution.

Access to Datasets

Organisations are experiencing data explosion in this data-driven culture. The outbreak of the pandemic also contributed to the ample supply of multiple sets of complex data from various devices. Access to datasets is one of the main factors to steer the power of Artificial Intelligence. Strategic data management is the crucial part of working with machine learning algorithms to reap the quality value of data. These models do not work unless there is an access to sufficient databases for training purposes for the future use. Large scales of historical, as well as current datasets are needed for machine learning models to solve complex problems and predict appropriate outcomes efficiently. Multiple industries are receiving consumers, patients and clients datasets with the help from digital transformation to boost productivity.

Integration of Artificial Neural Network

An artificial neural network is revolutionising the traditional machine learning models for the advancements in computer vision. It mimics the operations of a human brain with the help of the above-mentioned datasets. These factors are inter-connected in some unusual ways. Convolutional Neural Network, steers the power of Artificial Intelligence and deep learning into computer vision for better image processing to detect appropriate business insights for the organisations in this competitive market. Single Shot Multibox Detector and Generative Adversarial Networks are two of the important advancements in computer vision through these artificial neural networks.

Micro processing Chips

The semiconductor industry is focused on developing microprocessing Artificial Intelligence chips to provide strong bandwidth memory and on-chip memory in the upcoming years. It is one of the important factors to steer the power of Artificial Intelligence. These chips will enhance computer vision at a new level for organisations to deal with data management, efficiently. The AI chips have transformed the traditional CPUs into Graphics Processing Unit (GPU). It is essential for smart devices and machines for its tens and thousands of cores that help in the training of machine learning models.

Introduction of Open-Source Software

Multiple open-source software provides necessary data management tools to utilise in transforming complex data into meaningful outcomes. All these data management tools are 100% open-sourced that help the beginners, as well as the professionals with a source code. Data scientists and machine learning engineers use this incredible open-source software to reduce time in coding and focus more on generating the right solutions to predict the nearby future. This software also provides data visualisation tools to create interactive and colourful reports and projects with sufficient data and exports it to multiple devices.

Higher ROI

Yes, it is overwhelming to learn about Artificial Intelligence, computer vision, machine learning and data. But, business leaders are motivated to adopt cutting-edge technologies due to higher ROI. There is a bright future ahead in this cut-throat competitive market if an organisation adopts artificial neural networks and data management tools as soon as possible. Machine learning models are successful in boosting productivity, protecting employees from hazardous scenarios and understanding customer behaviour for better customer engagement. Leveraging the power of Artificial Intelligence with the compiled datasets, is providing a better chance of receiving higher ROI in the tech-driven world.

The post Key Ingredients to Power up Artificial Intelligence in 2021 appeared first on Artificial Intelligence.

Why explainability is key to success in machine learning

aiuniverse — Sat, 20 Feb 2021 05:45:26 +0000

Source – https://thepaypers.com/

Sean Nierat from PayPal has explained to The Paypers why there is a need of systems that not only make accurate predictions, but also that explain why they’ve arrived at a particular answer

Machine learning (ML) is part of a burgeoning AI industry that could soon become a multitrillion-dollar opportunity for global businesses. It is being used by PayPal, and others to help pioneer advanced data-driven fraud prevention by enhancing human intelligence with a 360-degree view of each customer. Yet, as ML becomes ubiquitous, it’s increasingly being argued that we not only need systems to make accurate predictions but also ones that explain why they’ve arrived at a particular answer.

Tackling bias with transparency

We’ve come a long way from the old days of fraud prevention. It’s undeniable that bad actors are getting smarter, with huge volumes of readily accessible customer data at their disposal and a wealth of tools bought on the dark web. Sophisticated fraud built on these foundations demands an equally sophisticated response. That’s why PayPal uses advanced ML to continually optimize the complex rules written by our client’s in-house fraud and data science teams, and to apply these rules to large datasets in order to spot patterns that humans may miss.

The problem with such systems is that they’re only as good as the data they’re trained on. Increasingly, organizations are concerned about unconscious bias emanating from this data, and the algorithms designed to interpret it. With ML used today in everything from mortgage application approvals to police facial recognition systems, there are important questions to answer – especially in a new era of intense regulatory scrutiny.

Clear box vs black box

This is where clear box ML or ‘explainable AI’ (XAI) approaches come into their own. Black box models like artificial neural networks (ANNs) or deep learning operate so that even the humans that designed them don’t know how decisions are made. However, with XAI, businesses gain vital insight into the whole process, from data collection to decision making.

This additional clarity and transparency offers multiple benefits including:

improves business confidence in an XAI-powered prediction/ outcome;
enhances the ability to control and manage algorithms in line with business objectives;
increases accountability, as systems can be audited;
improves regulatory compliance efforts;
enables teams to identify new fraud patterns faster.

A new approach

PayPal’s enterprise Fraud Protection offerings champion clear box, advanced ML through our use of explainability methods like LIME, Shapley, and RL-LIM. Our prediction engine delivers an interpretability plot for every single event, helping to drive customer confidence in the results and continued ongoing improvements.

Our platform is purpose-built to handle both the complex fraud challenges businesses face today and to make the necessary adjustments to help address those of tomorrow. With PayPal, businesses can take a dynamic approach to fraud – streamlining the experience for good customers and adding protection layers when necessary.

PayPal leverages fraud and risk knowledge from its 2-Sided-Network of over 330 million customers and 25 million merchants transacting 12 billion times a year as well as integrated third-party feeds to enable the processing and correlation of vast amounts of heterogeneous data to help deliver actionable business intelligence.

Here’s how:

a purpose-built data lake stores structured and unstructured data from various sources;
powerful Device Recon analyses hundreds of mobile and desktop device characteristics and behaviours, and applies machine learning models for risk scoring and clustering;
easy-to-update rules and machine learning algorithms help businesses adapt to changing fraud schemes;
robust link analysis and data visualization help enable businesses to proactively uncover anomalous patterns indicative of fraud;
real-time complex authentication helps differentiate trusted from suspicious users.

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Emotional Intelligence is the Key to a Successful Company Culture

aiuniverse — Sat, 08 Jun 2019 11:23:59 +0000

Source:- business.com

Determining a job candidate’s potential for success is a difficult task. In the past, hiring managers looked to people’s intelligence quotient (IQ) to gauge whether they would bring value to their organizations. Now, however, emotional intelligence (EQ) is seen as an increasingly relevant predictor for success.

Research shows that 95 percent of human resources managers believe that that EQ is critically important because it enables people to regulate their own emotions. Workers who are adept at coping with their feelings are more inclined to show empathy and understanding to their colleagues, and better at conflict resolution. Instead of needing constant oversight and mediation, they know how to tactfully address problems and how to encourage their coworkers’ best attributes.

Why emotional intelligence matters to culture

The definition of emotional intelligence is “the ability to recognize, understand and manage our own emotions” and those of others. Emotionally intelligent people are essential to a great corporate culture. You cannot build a dynamic, forward-thinking company if your entire team is constantly anxious, in conflict, or subtly (and not-so-subtly) sabotaging one another.

When you hire people based not only on skill and job experience but EQ as well, you nurture a self-sustaining culture of greatness. These people want to excel, and they’re self-aware enough to know where they need to improve. They’re confident in their abilities and committed to the company’s collective success, not just their own.

Therefore, they’re keen to connect with their peers and their clients, recognizing that they are more effective when they build real relationships. Then, when problems arise, they approach them rationally and compassionately, instead of pointing fingers and wasting the company’s time and resources.

How EQ manifests in the workplace

Here are the ways EQ shows up in strong teams:

Recognition of strengths and weaknesses: Great soccer coaches don’t just say, “This guy is a midfielder and this one is a goalie.” They understand why each person is in that position and can articulate exactly what skills they bring to it. They have a holistic view of how each individual works as part of the broader team.

Similarly, a good manager knows their departments inside and out. They know who works well together and why, who excels under pressure, and who needs a little extra lead time but will always knock it out of the park.

Any manager’s job is made easier by having employees that are emotionally intelligent. When the team leader provides feedback, they don’t take it as personal affronts. They know their strengths and weaknesses and are eager to improve. This dynamic makes for really growth-oriented, high-performing companies because everyone speaks the same language and focuses on the same goals.

Self-selecting A players: When you curate a team of A players — highly motivated individuals who have a high IQ — you build a culture that tolerates nothing less than everyone giving their best. If someone lands a new position and quickly starts slacking off, their peers will let them know. They ask if there’s anything they can do to help and try to bring them up to speed. If that person continues to slack, the culture will force them out.

A team of high-functioning professionals won’t allow anyone to ride their coattails or slow their company’s progress. You see this at big companies, like Netflix and Google, that have cross-functioning teams. Whether in marketing, operations, or design, you need to work with people across departments who keep up with a high-level work pace. There’s no room for people who are unwilling to work as hard as everyone else.

Self-awareness: Emotionally intelligent people ask, “What do I need to do to be the best me that I can be today? And who can help me do that?” Then they look for opportunities to collaborate with colleagues and leaders who will help them get better. When that is part of the culture, progress happens very rapidly. Everyone witnesses their peers leveling up day after day, which motivates them to improve, too.

People-centered service: My company emphasizes emotional intelligence among all of our people, and it shows in our services. Our financial advisors don’t just work with clients based on their numbers. They get to know their goals, ambitions, and insecurities.

In other words, they see the world through the client’s eyes and empathize with their positions. Then they recommend investments and tax strategies based on what’s right for that client. Never once have we prioritized products or numbers over client profiles. Every member of our team knows that they must understand the client before they can advise them.

Emotional intelligence reverberates throughout an organization. By prioritizing EQ in your hires, you build a motivated, dynamic team that supports one another and ultimately drives the company’s success.

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