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

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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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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

The post Is AI Contributing to Climate Change and Delaying People Coming out of Poverty? appeared first on ReadWrite.

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How to Ensure Data Privacy in the Time of Coronavirus

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The Coronavirus pandemic has brought about disruptive technology solutions to slow the spread of the virus and minimize its impact. Contact tracing utilizing mobile technology is one promising but controversial solution that has been tested in some countries and proposed in the United States.

The Washington Post reports that millions of people around the globe have already been placed under some form of surveillance in an effort to monitor people’s movements and attempt to trace the spread of COVID-19.

Of particular note, at least 27 countries have already started using data from cell phones to track the movements of their citizens. While this approach has a great potential to benefit the public good, the implementation and adoption of such technology raise important questions about transparency, AI ethics, and data privacy.

Fighting the spread of COVID-19 with smart technology.

Contact tracing can be one of the most effective ways to contain an outbreak. However, COVID-19 is not a typical outbreak. The virus is often transmitted by individuals who display no symptoms of the infection and may not even realize that they are carrying the virus.

Standard contact tracing usually involves individuals who are symptomatic and are aware they are carrying the infection in question. Because of this, traditional contact tracing methods are challenging and problematic.

The Big Data Institute at Oxford University has proposed a solution for a mobile contact tracing application that is much more agile, efficient, and scalable than traditional manual contact tracing methods. The team developed a mathematical model designed to stop the epidemic if implemented on mobile devices by a substantial portion of the population. It’s poised to reduce manual contact tracing from 72 hours to four hours. By replacing weeks worth of manual work performing contact tracing, the mobile application can slow the spread much more quickly than traditional methods.

For the proposed mobile tracing method to be effective, quite a few hurdles must be overcome.

First, the majority of the population – including symptomatic and asymptomatic individuals – must voluntarily opt-in and adopt the application. The research team at Oxford estimates that about 60% of the population would have to utilize the application for mobile tracing to reach enough new virus cases to make an impact on the spread of the virus.

Another challenge lies in widespread and accessible COVID-19 testing. For the application to work, the majority of the population would need to have access to reliable testing in order for the application to properly and fully survey potential outbreaks.

There is much evidence suggesting that the application of rigorous wide-scale testing coupled with the application of mobile technology can blunt COVID-19 infection and mortality rates.

Taiwan was able to contain the COVID-19 outbreak better. Why?

A recent Stanford report demonstrates how Taiwan – a country just 130 km from the epicenter of the outbreak in China – was able to contain the outbreak without the draconian lockdown measures in place throughout many of the advanced economies.

How did Taiwan manage to limit and contain the spread of the virus? The report highlights five interconnected factors: pandemic readiness, national electronic health records database, wide-scale testing, big data analytics, and the use of mobile technology to track movements of individuals who tested positive for COVID-19.

Within 72 hours of the outbreak, a comprehensive case identification protocol was instituted based on travel histories. Individuals determined to be high risk were monitored through their mobile devices.

Health authorities were then able to trace the movements of high-risk subjects and mitigate the risk of further transmission through targeted isolation measures. Taiwan may serve as a template for how to mitigate future pandemics.

Balancing public interest and data privacy rights.

Innovative initiatives are underway by major tech companies, notably Google, Apple, and Facebook, to track and analyze how the virus is spreading and to gauge the effectiveness of social distancing measures.

Facebook’s Data for Good project is designed to track the movements of users to measure and anticipate potential outbreaks.

In the context of the COVID-19 outbreak, researchers, non-profits and public agencies can leverage the data – which is anonymized and aggregated – to evaluate and implement strategies to slow the spread. However, such initiatives raise concerns about transparency and data privacy rights.

Data Privacy, Technology

There’s a strong case to be made that the implementation of mobile tracking applications represents an unwelcome intrusion to privacy rights.

When the processing of PII is necessary for the public interest, then it can be done without obtaining the data subject’s consent.

The GDPR includes specific guidelines for the use of data in the context of epidemics. Recital 46 of GDPR  stipulates that the collection and processing of personally identifiable information without data subject consent are acceptable when it is necessary for humanitarian purposes. The information includes monitoring the spread of epidemics and addressing humanitarian emergencies, such as natural or man-made disasters.

Tips for the transparent and ethical use of technology.

It is the responsibility of technology leaders and policymakers to implement ethically sound, transparent, and fair applications related to the use of AI-driven profiling technologies and the transmission of highly sensitive health information.

For mobile tracing applications to be widely adopted and therefore effective in slowing the spread of COVID-19, the first and most important step is the grain the public’s trust.

Principles of fairness and transparency related to artificial intelligence are re-enforced by the EU Commission on The Ethics Guidelines for Trustworthy Artificial Intelligence. Every organization should be committed to these seven fundamental guiding principles for the application of AI technologies:

  1. Human agency and oversight
  2. Technical robustness and safety
  3. Privacy and data governance
  4. Transparency
  5. Diversity, non-discrimination and fairness
  6. Societal and environmental well-being
  7. Accountability

These principles were further re-enforced by AI Now Institute in its 2018 report in which the organization asserted the importance of the public’s right to known which technology systems are impacting their everyday lives and how they are doing so. To that end, organizations must be transparent of the algorithmic systems used and their purpose, applications, and potential public impact.

Furthermore, while the use of technologies to help mitigate the impact of COVID-19 may be essential to protect public health and safety, they should meet the following criteria as proposed by the Electronic Frontier Foundation:

  • The use of such technology must be medically necessary, as determined by appropriate public health experts.
  • New processing of personal data should be in proportion to actual needs.
  • The use of such technology must be non-discriminatory and without bias; people must not be scrutinized due to nationality or other demographics.
  • Any new government powers that have arisen due to new measures must expire once the disease is contained.

Innovative technologies have the potential to accelerate and transform efforts to combat the spread of the novel coronavirus.

In order for these solutions to fulfill their intended objective and have the greatest positive impact, transparency and fairness are needed.

Before the public adopts new technologies at scale, they must trust these new and emerging innovations. The ethical use of AI should be evaluated not only on a legal basis but on a moral one as well.

It’s the only way for novel solutions to gain the people’s trust, inspire widespread adoption, and make the greatest impact – in times of crisis and in the post-COVID-19 world.

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