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How Can AI and ML Transform the Way We Read and Understand Data?

ai and ml understand data

Today’s business is ruled by data and data-driven understanding. How you understand the data and interpret the data into business decisions has a direct impact on your business conversion and growth. For a more precise understanding of data, today we have artificial intelligence (AI) and Machine Learning (ML) technologies on our side. No doubt, these technologies that mimic human reasoning can positively transform businesses and their strategies.

We need to understand the impact of AI and ML technologies have in shaping our understanding and capability to interpret data.

Data-Driven Personalization

Any business understands the importance of communicating with customers individually. Yes, thanks to the very nature of digital interfaces that opened up the tremendous scope of individual preferences and choices, your business communication must take into account the preferences of individual customers. The increasing importance of addressing individual choices for business conversion has forced many companies to focus on data-driven personalization measures.

Not only the large businesses but also the startups and small businesses increasingly understand the importance of having access to the relevant data for meeting the needs of visitors. AI can dig the available user data deeper and fetch out relevant patterns and insights that can be further utilized for data-driven decision making personalization. AI can also help to scale up such personalization efforts for every individual user.

Stop the churn rate.

A superb example of how AI can allow personalization in business operations can be found in the case of Starbucks. The global coffee chain brand designed 400,000 different types of emails created based on the data of individual preferences, tastes, and choices. Such well crafted personalized communication can help brands to create more engaging communication and conversation for business brands. The brand actually AI to decipher the volumes of data corresponding to customer preferences and choices.

Data collection and data-centric.

When it comes to smaller businesses and little startups, such as AI-based data collection and data-centric personalization may be a little expensive. But small businesses can embrace similar approaches to create very specific data-oriented marketing campaigns with short duration to boost business conversion and customer engagement. Such AI-powered data-driven campaigns can also help to lift the brand image of any company.

Generating Sales Leads from Data that’s Understood

For the B2B segment, business conversion highly depends on generating new leads. The B2B companies also need to depend heavily on tracking contact data and reaching out to them effectively through lead generation funnel. Most marketers agree to the humongous range of challenges B2B-based businesses face in doing this. This is where AI can play a great role in streamlining the process of lead generation through intelligent automation.

Artificial Intelligence (AI) powered lead generation and contact tracking solutions have the capability to make an analysis of the customer base along with important trends and emerging patterns. These trends, patterns, anomalies, characteristics, and various attributes can deliver important insights for optimizing websites and web apps. Thanks to AI-based optimization insights a website can venture to use better programming language, tools, features, and UI elements to generate more leads.

Analytics and you.

On the other hand, AI-based business data analysis can work hand in hand with big data analytics. This sophisticated and highly incisive approach to data utilization can easily help to discover ideal customers for a business. The interactions of users on web pages and corresponding data can be analyzed by B2B brands with the help of AI tools to produce the most relevant as well as actionable insights.

Analytical activities.

To make things easier for the businesses, AI, and machine learning technology for such analytical activities are now spotted in most of the leading analytics solutions across the spectrum. Simple Google Analytics can also offer highly result-oriented and precision-driven reports. Such technologies can easily know about the shortcomings and loopholes behind the decreasing motivation of traffic and readings of business conversion fallout.

Great analytics tools.

There are also great tools like Finteza that uses AI technology for monitoring website traffic on a continuous basis besides checking other crucial issues and irregularities. These tools can also improve your data security since by detecting bad traffic they automatically point out the vulnerabilities in the web app.

Poor web traffic often results in DDoS attacks, manipulation of website cookies, and hackers or malicious programs impersonating computer bots. An AI-based lead generation solution can also reduce these security vulnerabilities.

Optimizing the User Experience (UX)

AI optimizes the scope of personalization in a data-driven manner and that is portrayed as the principal useless of AI in dealing with data. But AI is also highly effective in optimizing the web design and improving the user experience (UX).

User Behavior

AI achieves this optimization and improvement by analyzing user behavior and interaction data and user feedback. Machine learning programs particularly can play a very effective role in learning from user behavior and adjusting various interactive elements accordingly.

AI and ML programs running behind the scene basically collect a lot of data corresponding to real user behavior so that real-time feedback about shortcomings and improvement needs can be communicated to the business owners. An ML-based program can also bring instant tweaks to the UX attributes for better engagement.

Another important thing in this respect that needs to be explained is the great role of AI in improving the efficiency of A/B tests. In the A/B testing process the AI and machine learning can deliver the most important insights about user demands and preferences to take further enhancement measures for UI and UX.

The most important aspect of AI in making an impact over A/B testing is that it leaves no scope for vague assessment or guessing. The data-driven insights guiding the A/B testing is more possible now as website cookies provide clear insights concerning user behavior.

Based on such insights the landing pages can reduce form fields as per user interest and preferences.

Biometric Data Pushing for Enhancements

Biometrics data corresponding to direct interactions with a web app can help developers and marketers with a lot of actionable insights. There are many advanced online services right now available in the market that can help to understand and decipher website data.

Biometrics data coupled up with AI and machine learning technology opened up new possibilities for improved user experience.

Among these available services for data interpretation mostly take the help of a combination of both artificial intelligence and machine learning. These sophisticated solutions can easily track the eye movements of the users.

In addition, some of these services can also track facial expressions to assess user responses in different contexts. These services can extract the most organic kind of user data and generate the most valuable insights that can be used for UX design and performance optimization of websites.

Conclusion

As the trends stand, from this year onward the AI and ML-based data analytics and data-centric optimization of business apps will have more dominance. Thanks to these two technologies, there will be the least guesswork for all design, development, and optimization decisions.

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Private Fund Data Operations Should Provide More Protection

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COVID-19 has hit business leaders. It’s hit them with an endless parade of unexpected challenges. Leaders in private fund data operations are feeling it too. However, these challenges also present opportunities for technological advances.

Stretched Operations

Shrinking revenue and smaller budgets have reduced the margin for error. Risks are more severe. New risks are joining the ones that lurked in the shadows.

The time it takes to double-check numbers and make sure data operations are running smoothly is no longer a luxury. Fund administrators serving alternative investment funds, private equity, and venture capital are being stretched.

Exacerbating this are limited partners who are incredibly vigilant in checking investment reporting and statements.

Instead of relying on legacy systems that are dangerously susceptible to human error, this is the time to transition to an integrated platform that consolidates key data, automates processes with machine learning and adds clarity to data operations.

Operations Are A Worthy Investment

Moving away from point-to-point legacy systems to automated processes increases the speed of operations. And, it mitigates the risk of mistakes. Think about trying to say something fast and efficiently to a large group of people through a game of telephone.

The message gets garbled by the time it gets to its destination. So, moving to an integrated platform is like replacing the game of telephone with smooth, simultaneous communications. Get everyone on the same page.

Accuracy counts. It’s important to ensure your systems for managing data and fund operations are rock solid. Investing in good technology designed to improve operational productivity makes the business’ odds of running smoothly better. It will strengthen the business for what’s next when we’ve emerged from the crisis.

For many employees, remote work may end up being permanent. The accommodations being made now are becoming the norm. It’s crucial that the business’ operational systems sustain that transition as well. The move to automation can largely eliminate the risk of human error.

It can improve accuracy. However, that’s only true so long as there are checks along the way. Automation can also spread and multiply human errors if it’s not done right. Just look at Excel Macros, and cut and paste.

The goal is to remove human error from the data management equation. But, that does not equate replacing employees with automation. In fact, it’s a matter of putting energy where it’s needed, and in a way that’s simple.

The Best of Both Worlds

Get more simplicity out of complex internal systems. A fund can do it through automation. That’s even more important when employees’ efforts and the company budget needs to be aimed at keeping good investors calm.

In a crisis, human empathy is in high demand. If your team’s focus is on untangling accounting puzzles, their attention is not on the investor.

You’re responding to clients’ demands and getting more data online. You’re adding clarity. However, that also means clients need assistance adjusting to receiving services online. And, the volume of customer questions will only be amplified by the transition. Human connection is a vital tool.

Moving the management of business operations to an integrated platform frees up employees. It allows them to support customers in more personal, empathetic ways.

We’ve all found ourselves pounding “0� on our phone keypads in frustration to get past automated answering systems. Really, we want to speak with a human. In moments of crisis, people expect urgency, accuracy and empathy. They want to see clearly what is going on.

Implementing modern private fund data operations can deliver both. Automation means smarter, faster and more accurate data operations.  Employees are freed up to engage with customers.

Digital Doesn’t Have to Mean Complex

In a time when doubts are all around, business leaders still have a window to invest in security. That also means technological investments should be well researched. They should be checked ahead of time. Make sure they serve to improve the success of operations.

It’s essential that any integrated approach a fund goes after should first try to be more intuitive and easier to learn.

Funds need to integrate information from different places. And, they need to do it across the organization. So, funds have the general ledger. They have reporting systems, human resources systems, and market intelligence systems. None of these talk to each other. Really, they need to figure out a data integration strategy.

Before funds can ponder dashboards for people outside the fund, they need to consider internal interoperability. They need to consider ease of use. To move in this direction, they need to try to implement newer cloud-based solutions that will enable them to assemble data across the organization.

Everyone On Board

A good way to measure the success of new private fund data operations is how many employees are using it after integration. So, make data easy to get to, and easier to use by more people. That can help crack down on internal bottlenecks. Those jams can crop up when only a few people have a sincere understanding of how the system works. That limits progress.

Limited partners are focusing on capital statements. In fact, they’re relentless. But switching to an integrated platform can guarantee that statements will be error-free.

By taking advantage of the opportunities for technological wins, and investing in data smarts and fund operations, business leaders are aiming their businesses to emerge from this crisis stronger than before.

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Is AI Contributing to Climate Change and Delaying People Coming out of Poverty?

AI delaying people in poverty

AI has become the buzzword of the world, and an integral part of almost every company’s digital transformation agenda. AI users have become producers of AI tools and services. Corporate leaders and even the White House have come with forward with a directive on promotion, promulgation, and advancement of artificial intelligence.

On February 11, 2019, President Trump signed Executive Order 13859 announcing the American AI Initiative. Executive Order 13859 is the United States’ national strategy on artificial intelligence. 4000+ companies in the world are in the business of producing AI tools, products and services.

AI and its impact on climate change.

 

Gartner projects that by 2021, AI will create $2.9 trillion in business value and generate 6.2 billion hours in worker productivity. In light of these assumptions, it’s no wonder that PwC forecasts that A.I. could contribute $15.7 trillion to the global economy by 2030.

While the whole world is optimistic about AI-led use cases and championing huge investments, one thing is probably getting far less attention than it ideally should: AI – induced climate change.

Artificial intelligence advancement needs two key ingredients (other than passion):

  1. Tons of training data.

    Machine learning models are nothing if there is no historical data to train them. Example, to train computer vision models to detect, identify and label objects that they see if a photo or a video, they need to be trained on corresponding, labeled and annotated data first for elongated periods of time, till they begin to use the learnings to then identify objects they might have seen before.

  2. Correspondingly a lot of storage and computing power.

    Now, multiply this with millions of use cases companies are developing using computer vision to detect, identify, label, and then predict some, to get an idea of the amount of infrastructure being used.

At a very conservative level, there were more than 4000* companies around the world in 2018, and growing, dedicated to developing one or more specific uses cases using AI to automate human work in one form or another.

What these 2 ingredients mean is that humungous amounts of energy requirements to store, backup and feature engineer this training data.

To give you an idea, few brainy folks @ University of Massachusetts Amherst measured that an average car produces 126,000 pounds of Co2 over its lifetime. Training a single transformer model to achieve acceptable levels of accuracy with 1 GPU over a neural architecture requires energy that would produce 626,000 pounds of Co2.

That amount of Co2 is approximately equal to about 5 cars running their engines altogether for 10 years. That stat is teaching us that merely to train a single transformer model for AI is 5 cars running for 10 years.

Extrapolating this to model building and training showing that those 4000+ AI companies are doing 24/7, it’s mind-boggling to see equivalent Co2 production and also the energy diversion towards ML training.

3 obvious questions arise.

  1. How ethical is this AI process — especially when more than 940 Mn people in the world do not have access to electricity?
  2. When corporations say they support ‘green’ initiatives, how credible is that statement, when the same corps invest billions into AI? How is it possible to be still green when you invest in AI research? Google seems to be interesting– it uses 56% of its total power needs via renewable sources of energy. In comparison, Microsoft is at about 32% and Amazon at 17%. (Cook et al., 2017.)
  3. Is AI progression worth it, given the climate change impact due to such levels of Co2 production & energy diversion away from the lower sections of society?

The counterargument that AI progression is creating more jobs.

So we have the counterargument that AI progression is creating more jobs and helping countries achieve a higher GDP and per capita rate. Is this a valid claim? Yes, but what remains to be examined closely is whether this is indeed new job creation or re-skilling existing manpower.

The argument with every wave of technology, from the automatic weaving looms of the early industrial revolution to the computers of today, is that jobs are not destroyed, but rather an employment shifts from one place to another as entirely new categories of employment are created.

The Luddites might have wrecked the mills as a protest against machine-enabled automation, but today, those same workers would be defending manufacturing against the disappearance of those jobs.

So are these additional jobs helping lift individuals out of poverty?

More importantly, are the additional jobs — if any. Are these supposed extra jobs actually lifting people out of poverty? Can someone work and finally have the money to finally get access to electricity? My thought is — is this the same electricity they could have had in the first place — had it not been diverted for purposes of advancing AI? Think about it.

Climate Change

Perhaps, the most fundamental of all is — What is it with AI that we can achieve that’s MORE important and precious than preserving our climate?

Is investing in AI a really good bet for a socially responsible leadership? I don’t have an answer to this, but am often intrigued by this question.

I interestingly belong to that category of the society which has been propagating the cause of AI, and I am at my wit’s end to find balance against this question. Please weight in with your thoughts on this subject. I’d love to hear from you.

These are personal views, and do not represent those of my current or previous employers.

Top Image Credit: Denniz Futalan; Pexels

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The Value of AI-Based Visual Inspection in 2020

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For over a decade, manufacturers have turned to automated solutions to improve their bottom line. Automation and machine vision are now being augmented and even replaced by AI. Here is the value of AI-based visual inspection in 2020.

Value of AI-Based visual inspection

Being replaced by AI is especially true when it comes to visual inspection. The use of AI-based visual inspection technology is transforming manufacturing’s ability to improve business operations.

AI-based visual inspection relies on two of AI’s main strengths: computer vision and deep learning. Every AI system is built with the core capacity to perceive its environment (computer vision) and act on those perceptions (deep-learning).

As a result of deep-learning, AI adapts to a range of environments, making it useful across a multitude of industries. It has unlimited potential and can be developed rapidly to meet a manufacturer’s needs.

Concept of AI-based visual inspection

Well-trained human eyes can detect defects. A well-trained AI-based vision system can do the same — but with greater efficiency. Like a human eye, AI-based vision systems capture an image and send it to a central “brainâ€� for processing.

Like a human brain, an AI “brain� makes detailed meaning from the image by contrasting it against its existing knowledge.

AI-based vision systems are made of two integrated components. A sensing device acts as an “eye,� while a deep learning algorithm acts as a “brain.� The integrated system successfully mimics the human eye-brain ability to interpret images.

AI-based vision systems are more efficient than human eyes because the AI “brain� stores greater amounts of information.

Robust computational power can parse through available data at rapid speeds. The system can classify objects in both photos and videos and perform complex visual perception tasks.

AI-based vision systems can search images and captions, detect objects, and classify multi-media.

Thanks to deep learning-based visual processing, AI-based visual inspection systems can perceive cosmetic flaws and detect defects across general or conceptual surfaces (mobidev dot biz).

Benefits of AI-based visual inspection

1. Fast Implementation

Decades-old automated systems depend on defect libraries, lists of exceptions and complicated filters. The time it takes to accrue this information, clean it for accuracy, and re-implement it decreases its efficacy. It also wastes labor.

AI and deep learning do not require prolonged programming or tediously lengthy algorithms. AI-based visual inspection systems might be constructed by several quality engineers and a dataset of training images. The system learns rapidly and is integrated over several weeks.

2. Improved Analytics and Quality Control

Manufacturers can use AI to document inspection results and to assess product quality. Some overall process improvement initiative metrics that can be successfully tracked and correlated with concrete vision data include:

  • process recipes
  • equipment differences
  • component suppliers
  • factory location

In addition, inspection images and results can also be tracked and documented. These initiatives prevent future failure, which saves time and additional production costs. Applying deep learning-based machine vision across all initiatives and inspections helps manufacturers recognize and address defects early.

3. Labor Costs Reduction

AI solutions have higher rates of consistency than most expert human inspectors. Human inspectors must be trained and are only able to maintain a high degree of focus for 15-20 minutes at a time. Labor costs are incurred yearly and staff turn-over is an issue. For these reasons, AI-based vision inspections are more cost-effective than manual labor.

Use Cases

AI is increasing the competitiveness of manufacturers across every industry. Here are recent use cases from the aviation industry, semi-conductor manufacturing sector, and bio-science.

Alibaba has risen to meet healthcare challenges created by the coronavirus. Alibaba’s deep-learning-based visual recognition system is capable of detecting the coronavirus in chest CT scans at a 96% accuracy rate. The system accessed 5,000 COVID-19 cases and can provide a diagnosis within 20 seconds. Moreover, the system can differentiate between images of viral pneumonia and images of coronavirus.

Fujitsu Laboratories implemented an Image Recognition System at Fujitsu’s Oyama factory. The system ensures that parts are produced at optimal quality levels by supervising the assembly process. The system was so successful that Fujitsu implemented it across the entirety of the company’s production sites.

Airbus introduced an automated, drone-based aircraft inspection system in 2018. The system has improved the quality of inspections and reduced aircraft downtime.

GlobalFoundries is a leader in semiconductor manufacturing. The company designed a visual inspection system that detects defects in a scanning electron microscope (SEM) images. The system detects defects in a wafer map which then helps to determine the semiconductor device’s performance.

The use cases listed above reveal the extent to which AI is capable of automating many aspects of our lives. Although AI vision will never replicate human vision, the technology continues to classify information and advance in ways human eyes and brains cannot. And only humans might consider how to use this technology to get advantages.

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