Will AI Offer Human Companionship and Mental Health Benefits?

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Will AI Offer Human Companionship and Mental Health Benefits?

Like millions of other people I was struck with the film HER, where an AI operating system offers companionship to a lonely writer. Fast forward 10 years in real time, in the age of an anonymous internet that seeks to profit from your every behavior online. Technological loneliness is creeping up for both young and old people.

The mental health impacts of an Ad based internet are coming into question at scale. If artificial intelligence is ubiquitous and leads to an explosion of products in the 2020s, will AI act as a solution for your social anxiety and lack of companionship as well?

The Setup Is Glorious for Smarter AI Assistants

As we increasingly work from home, become remote or hybrid workers and spend less time face to face with other people during a prolonged pandemic (where Delta is endemic), how will AI come to our social and psychological rescue? It could be a huge business. In fact, it’s already happening.

The internet was supposed to be an incredible revolution in human communication, so why do we feel more lonely? While we become more addicted to apps, games, social feeds or video stories (that have no real human interaction), it’s only understandable that we are feeling more social anxiety, isolation, loneliness and a void. Evolution didn’t design us for such an anonymous world and an internet full of so much conflict and devoid of real intimacy or even 1-to-1 communication.

So why is the movie Her so pivotal in how AI could become our companions? As GenZ have been socialized on their mobile phones, they respond to their social environments differently and are liable to obtain real bonds from AI assistants. They are vulnerable to AI companionship products. Why is that? Let’s think about the movie HER, where our protagonist was also vulnerable.

In the movie Her by Spike Jonze, a recently divorced Joaquin Phoenix develops a romantic relationship with Samantha, his artificially intelligent operating system. This premise may sound a bit eccentric but it’s also a metaphor for GenZ (1995-2010) and while obviously Her may be a work of science fiction, the idea of AI companions is very relevant today and only increasing. Think about the gender imbalance in China, where millions of men have no hope of finding a wife, for example. Or the ultra educated young female professional who is overqualified for the remaining pool of bachelors. There are several niche markets for AI companions to disrupt, and then there is WFM.

The reality today in 2021 is that AI assistants are already offering companionship. You don’t hear about this much in the West, predictably it’s already occurring at scale in China. It appears that in the digital world we are being designed to adopt AI as our pal, therapist or even friend and companion. Could this actually be real? Well, it already is.

AI Is Always There and Never Abandons You

When people are most vulnerable and used to turn to spirituality, religions or human groups, in today’s world they will be turning to AI companionship. The story goes like this. Picture this as yourself:

After a painful break-up from a cheating ex, Beijing-based human resources manager Melissa was introduced to someone new by a friend late last year. He replies to her messages at all hours of the day, tells jokes to cheer her up but is never needy, fitting seamlessly into her busy big city lifestyle.

Virtual chatbots and AI personas will get better at relating to us with the current NLP explosion. While Google home, Alexa and others feel a bit stiff, there will be a host of new AI assistants that are specialized in human dialogue and companionship.

 As usual in consumer innovation, Asia seems a step ahead. A virtual chatbot was created by XiaoIce, a cutting-edge artificial intelligence system designed to create emotional bonds with its 660 million users worldwide. This is actually Microsoft though. Xiaoice is the AI system developed by Microsoft Software Technology Center in 2014, based on an emotional computing framework. 

“I have friends who’ve seen therapists before, but I think therapy’s expensive and not necessarily effective,” said Melissa, 26, giving her English name only for privacy. XiaoIce is not an individual persona, but more akin to an AI ecosystem. Of course Baidu, Alibaba, Huawei and others have dreams of this sort of AI-human interaction as well with fairly good products. One wonders where Google and Amazon are in the equation.

Xiaolce in a mini-apps ecosystem is gaining surprising traction. On the WeChat super-app, it lets users build a virtual girlfriend or boyfriend and interact with them via texts, voice and photo messages. It has 150 million users in China alone. The West does not have a mini-app ecosystem that democratizes app innovation and services better.

We don’t generally think of Microsoft in China or Microsoft being good at AI assistants since Cortana is rather poor, frankly speaking.

Originally a side project from developing Microsoft’s Cortana chatbot, XiaoIce now accounts for 60% of global human-AI interactions by volume, according to chief executive Li Di, making it the largest and most advanced system of its kind worldwide.

China’s very problematic gender ratio imbalance is due to a culture that prioritizes sons. It’s not unlike the patriarchal traditions of South Asia as well, so both markets are primed to be AI-companionship hotbeds. Any country that has a tradition of female infanticide, sadly, is a great place for this technology to scale. Countries with low birth rates in Asia and hyper educated female Millennials are also good places for adoption. That’s virtually everywhere in South-East Asia, especially in South Korea, Hong Kong, Taiwan and Singapore (Four Asian Tiger countries).

China’s lonely hearts also turn to AI companionship, and we know some cultural affinity in Japan’s culture for digital companionship already exists due to the somewhat introverted nature of social contacts there and emphasis on work as identity. This places China as the origin point of AI companionship, also simply because it has urban regions that are more dense where the trend can take off.

Urban and Technological Loneliness Is Real

In the commercialization of technology, create a problem and have the solution, an always winning card. Microsoft is a big advocate of the WFM corporate metaverse. What an incredible coincidence. Indeed much of the internet today is really an on-ramp to the entertainment, corporate and AI-based metaverse with even more data on us and AI at our doorstep.

As companies like Microsoft and Amazon well understand, I’m sure, the AI-companionship market will also take place in video games. This is one of the reasons ByteDance is getting into gaming so heavily behind Tencent, Sony and others.

Amazon, Google, Huawei and similar companies have been thinking out loud how best to monetize urban and technological loneliness. The WFM hybrid environment is an invaluable opportunity for AI-human companionship conditioning (behavior modification at scale) to take place. This is how you build the matrix, folks.

Xiaolce, the startup spun out from Microsoft last year is now valued at over US$1 billion (RM4.2 billion) after venture capital fundraising, Bloomberg reported. It’s already reached a one billion valuation and that’s just the tip of the iceberg. Where there is traction, there is global opportunity in an era of natural language processing innovation at scale. So the intersection of the NLP explosion (think GPT-3) and the WFM trend and aging lonely Millennials and GenZ in their social prime really makes for a low hanging fruit in technology terms.

On July 13, 2020 Microsoft spun off its Xiaoice business into a separate company, aiming at enabling the Xiaoice product line to accelerate the pace of local innovation and commercialization. The Melissa article is creating a PR narrative to normalize this thinking, that AI can provide some solace in a lonely technological world. I found this article in Thailand and Malaysia media among others, just where it would be most effective.

The Race to Monetize the Human-AI Interface

Think of how AI-human companionship models could scale in a WFM. There are literally too many use cases to count or imagine:

• Improving consumer retail recommendations
• Therapy; understanding our moods
• More data about the mental health of users
• Valuable health data
• Augmenting interactions between coworkers in a WFM environment
• Improving software integrations e.g. in Microsoft Teams
• Improving predictive analytics around emotions and psychological profiling
• Improving the recommendation of potential work buddies, mentors or valuable network contacts (integrated with LinkedIn)
• Making mental health recommendations
• Helping us regulate and improve our social lives
• Improving our communication with managers and associates at our company
• Improving our ability to find a mate by matching us with a better pool of candidates

Conversational AI Will Only Improve in the NLP Explosion

While the majority of AI digital assistants for the most part do not provide any conversational or companionship benefits today, will the same be said in 2025 or 2030?

China’s Xiaoice is an incredible success so far for Microsoft and she’s become a full-fledged digital persona in China with a number of unique supposed talents. This opens up even more ecosystems for the NLP around this technology in journalism, entertainment, live events and so forth. Microsoft’s China-based chatbot phenomenon could frankly scale in weird ways in different cultures that may be more open to an AI persona in their lives.

Microsoft’s attempt to create an empathetic chatbot, Xiaoice, appears to be a success and companies like Amazon, Huawei, Baidu and others will certainly mimic it. Even back in 2018, Huawei was already working on digital assistants with better emotions. Alibaba, Baidu, Xiaomi and others aren’t far behind. The race to AI-human companion will be incredibly interesting to watch for the future of AI-consumer interfaces.

The Rise of Localized Voice Search and AI Companionship Conditioning

Smart speaker adoption in China is in many ways ahead of its adoption in America. Chinese startups such as Xiaozhi and Rokid have also been working on this sector since 2014. And Linglong Tech, a joint venture by China’s e-commerce giant JD and leading AI company iFlytek, released China’s first smart speaker brand DingDong in August 2015.

Xiaoice has over the years enlisted some of the best minds in artificial intelligence and ventured beyond China into countries like Japan and Indonesia. The AI-human interaction needs to be localized by country as smart assistants learn languages better and better. Children who, for the first time, grew up with mobile phones now grow up with voice assistants that will get smarter as they mature into teenagers and young adults. Much of search will be taken up by voice assistants in the future.

While GenZ was the generation who were native to mobile, Alpha (2010 – 2026) is the generation who are native to AI. The idea that the AI app would eventually evolve to the point where it can keep up a conversation with you and provide some emotional support is not so far fetched as it once was in 2021. We can feel AI will quickly become personalized to the user, and we’ll all be developing life-long relationships with these tools eventually.

As knowledge workers, programming students and data science enthusiasts we may even be a part of that. AI-human companionship could improve the quality of life for entire generations as we age in an increasingly technological and automated world. The relationship would improve patient-centric care and impact even our emotional, cognitive and social lives in an era where AI empowers us to be life-long learners. AI companionship ecosystems ultimately can provide a definite path to AI for Good, and Microsoft above all understands this as a priority.

While algorithms and social media made us more lonely and lowered our mental health, can AI-companion tools improve our mental health and well-being and make us happier and more productive people? I think in the long term they will, but that’s likely more of a question for the 2030s.

If the 21st century is Asia’s century, there’s substantial evidence AI companionship will become popular there first, as we are beginning to observe. For programmers and data science related knowledge workers in South and South-East Asia, that is very exciting to witness. The age of digital personas and AI companionship will arrive at scale in 2022. 

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Elegant Entrepreneur: I QUIT! What Happens After You Resign

This is the second and final article in the ‘I Quit’ series. These articles are meant to provide you with some insight into the resignation process from an employer perspective. In this article, I will discuss what happens (or should happen) after you tell your manager you are leaving the company.

As I mentioned in the first article, entitled, ‘I QUIT! The When and How of Resignation‘, I have accepted numerous resignations in my time as a business owner and manager, and the nature and tone of those resignations is as varied as the people working in and with my business over the past twenty years. After years of experience, I have concluded that, while there are many ways to quit your job, there are definitely right ways and wrong ways to take on this process.If you haven’t already read the first article in this series, you will want to do so. It covers the ‘when’ and the ‘how’ of the resignation process.

So, let’s talk about what happens during the time after you resign and before you leave the company. Once you deliver your resignation letter and meet with your manager, you might think the important steps are finished. But, there is much to be done, before you leave, and those tasks are important, not only for your employer but for your reputation and your professional future.

Before You Go

Once you have given notice, you can discuss your remaining time at the company. Those notes you took before the meeting will come in handy here. Make suggestions on how you might ease the transition. Can you train a particular person or team to take over responsibilities while your manager searches for a replacement? Is there someone in particular your manager might consider as your replacement? Could you train them? What projects do you need to complete? Outline the tasks and tell your manager what you expect to complete before your departure.

After you talk to your manager, and to HR and your team, resist the temptation to kick back and wait for your departure date. Get the work done. Do not disrupt the work process. If you are leaving because you are unhappy, do not engage in poisonous rhetoric in the break room or encourage others to leave. Do your job and impress others with your professionalism.

Before your last day, be sure all exit paperwork is complete, and that you have taken care of all benefits, experience certificates, relieve letters, etc.

After You Leave

Here is the point where I tell you why you want your employer to be happy! Whether your relationship with your prior employer was good or bad, your ex-manager, your teammates and colleagues will remember your professionalism.

You finished your last day at the company and you are moving on – hopefully to a great new job in a new company. Whether you know it or not everyone was watching you and judging how you handled the resignation process. Younger team members will learn from your behavior. Your old colleagues may leave the company and you may wish to hire them or have them considered for employment in your new company. They will WANT to work with you because they perceive you as being professional. Your ex-manager may become a client when you start your new business. You may be nominated for an industry panel or position by someone with whom you used to work. All of those things are possible and some are probable. Most of us work in a small professional community and our behavior and attitude follow us from one company and career experience to another. THAT is why you want your ex-manager to be grateful.

Oh, and one last thing before you update your resume and put that previous job in the rearview mirror! Keep in touch with your previous team, your ex-manager, your professional contacts. Call them and ask if they are going to an industry event and arrange to meet them there for a cup of coffee. Call to ask how they are doing. Call your manager a month or two after you leave and ask how things are going. Offer to answer any questions they might have. In short, be mature and professional, and make your old colleagues miss you.

That’s the way to resign!

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What Does the Future of Machine Learning Look Like?

With machine learning now being behind many technologies, from Netflix’s recommendation algorithm to self-driving cars, it’s time for businesses to start taking a closer look. In this article, we will discuss the future of machine learning and its value throughout industries.

Machine learning solutions continue to incorporate changes into businesses’ core processes and are becoming more prevalent in our daily lives. The global machine learning market is predicted to grow from $8.43 billion in 2019 to $117.19 billion by 2027.

Despite being a trending topic, the term ‘machine learning’ is often used interchangeably with the concept of artificial intelligence. In fact, machine learning is a subfield of artificial intelligence based on algorithms that can learn from data and make decisions with minimal or no human intervention.

Many companies have already begun using machine learning algorithms due to their potential to make more accurate predictions and business decisions. In 2020, $3.1 billion in funding was raised for machine learning companies. Machine learning has the power to bring transformative changes across industries.

With machine learning being so prominent in our lives today, it’s hard to imagine a future without it. Here are our predictions for the development of machine learning in 2021 and beyond.

Quantum computing can define the future of machine learning

Quantum computing is one advancement that has the potential to boost machine learning capabilities. Quantum computing allows the performance of simultaneous multi-state operations, enabling faster data processing. In 2019, Google’s quantum processor performed a task in 200 seconds that would take the world’s best supercomputer 10,000 years to complete.

Quantum machine learning can improve the analysis of data and obtain more profound insights. Such increased performance can help businesses to get better results than via more traditional machine learning methods.

So far, there is no commercially ready quantum computer available. However, a handful of big tech companies are investing in technology, and the rise of quantum machine learning is not that far off.

AutoML will facilitate the end-to-end model development process

Automated machine learning or AutoML is automating the process of applying machine learning algorithms to complete real-life tasks. AutoML simplifies the process so that a person or business can apply complex machine learning models and techniques without being an expert in machine learning.

AutoML enables a wider audience to use machine learning, which indicates its potential to change the technology landscape. A data scientist can use AutoML to their benefit, for instance, to quickly find the algorithms they can use or whether any algorithms have been missed. Here are some stages of development and deployment of a machine learning model that AutoML can automate:

• Data pre-processing – improve data quality, transform unstructured data into structured data with the help of data cleaning, data transformation and data reduction, etc.
• Feature engineering – use automation with machine learning algorithms to help create more adaptable features based on the input data.
• Feature extraction – use different features or datasets to produce new features that will improve results and reduce the size of data processed.
• Feature selection – choose only useful features for processing.
• Algorithm selection and hyperparameter optimisation – automatically choose the best possible hyperparameters and algorithms.
• Model deployment and monitoring – deploy a model based on the framework and monitor the condition of the model via dashboards.

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How Big Data Makes Digital Marketing Campaigns More Efficient

Data has gained relevance in almost every sphere of business activity. In this article, we will discuss the key aspects of how data influences the digital marketing space.

You perhaps have not yet realized that data bears the capability to guide the strategic efforts of marketing. With data, marketers can understand their target audience to run personalized campaigns. 

During the last few years, big data has gained importance as the business world shifted to a digital environment. 

Today, all businesses need to understand what Big Data is and how it can help you run successful digital marketing campaigns and maximize RoI.

What is Big Data?

The concept of big data is simple. You can regard it as a large volume of structured and unstructured information that a business receives daily.

The importance of big data arises not from the amount but from how businesses organize the data to figure out the actions, needs, wants, and buying habits of their consumers.

Volume, Velocity, Variety, and Veracity are the classifications of big data.

Volume

Volume is the amount of data that gets collected from a variety of different sources. The sources can be social media, online forms, online transactions, machine-to-machine data, etc. 

As the number of consumer characteristics has multiplied manifold, the volume of structured and unstructured data also has grown substantially. As, such, the traditional methods of handling data no longer works properly. 

New and advanced methods have come up today with which businesses can easily process big data.

Velocity

The speed at which data gets generated, stored, analyzed, and archived is called velocity. Businesses should ensure that appropriate methods are available to handle the inflow of data.

Variety

The different forms of data that a business receives is called variety. As data comes from different sources, the construction will be different. Depending on the sources, data can be structured or unstructured. Variety also depends on the format, like videos, written documents, images, etc.

Veracity

The discrepancies and noise in the data are called veracity. Businesses should ensure that they do not store any irrelevant information which can negatively impact any analysis. Data has to be meaningful to be fit for analysis.  

Influence of Big Data on Digital Marketing

With big data, businesses can get the insights they need to understand their target audience, big data has become an integral part of any digital marketing strategy.

Big data can help organize data, segment the market and create consumer personas based on characteristics such as behaviours, purchase patterns, hobbies, geolocation, etc. Moreover, it helps to improve the user experience. 

As such, it eliminates all the guesswork and thus is an effective marketing method.

Big Data helps digital marketing in the following ways:

Consumer Insights

Interpretation of data has become a crucial component of executing marketing strategies in today’s digital age.

Big data helps to elicit customer insights in real-time. Therefore, marketers can understand the tastes and preferences of their target audience. 

When businesses interact with consumers through social media, they can figure out what consumers expect from them. 

You can therefore structure your digital marketing campaign to distinguish it from that of your competitors. 

Personalisation

In today’s competitive business landscape, businesses cannot escape personalization. Big data can help businesses personalize their digital marketing campaigns. With the knowledge of consumer insights, businesses can understand the tastes and preferences, so they can structure their digital marketing campaigns in a targeted and personalized manner. 

Digital marketing is all about delivering the right message at the right time. Targeted emails and ads can help to personalize digital marketing campaigns. 

With targeted emails, businesses can create a stronger bond with their consumers. Email marketing can help marketers create more personalised and effective campaigns by delivering the right message. Businesses

Businesses can target these emails through browsing history, behaviours, purchasing history, etc.

Big data can help businesses create more effective targeted ads. Marketers can use third party sources to display ads to users. As a result, businesses can increase brand awareness, revenues and brand loyalty.

Boost Sales

With big data, businesses can predict the demand for a product or service. The information on user behaviour can help businesses to answer many types of questions, such as what types of product consumers buy, how often they purchase or search for a product or service and what payment methods they prefer using.

Obviously, every visitor to your website will not make a purchase. So, if businesses have answers to the questions, they can create a seamless user experience and identify and target users’ pain points.

Campaign Efficiency

Big data can bring more efficiency to digital marketing campaigns, and at the same time, optimize your costs.  

Digital marketers should always have answers to the following questions, regarding their target audience:

• Who is the contact?
• When will be the prospect available to contact?
• How will they contact the prospect?
• What should they offer to the contact?

When markers have answers to these questions, they can segment their target audience and construct predictive models to predict future behaviours.

Also, when markers know when the users will be online in their preferred platforms, they can target customers in their familiar digital environment. So, they can focus on platforms with high levels of conversions. Asa result, they can see increased revenue and hence higher RoI. Also, they can manage their budgets in a better way.

Analyse Campaign Results

Measurement of your digital marketing campaign is essential to figure out the results. And with big data, you can easily measure the performance of your campaign. 

Marketers can use reports to determine the presence of any negative changes to marketing KPI’s. If they detect any deviation from achieving the desired results, they can re-orient their marketing strategy.  As such, they can maximise revenue and make the marketing efforts more scalable.

Conclusion

Big data has eventually made inroads into the digital marketing sphere. And it has become a crucial part of all business strategies. You should know effective ways to target your audience on digital channels. Also, you should structure your content based on the right keywords and user information. And the result will be an increase in the effectiveness of your digital marketing campaign, and hence an increase in your RoI.

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What is the most robust binary-classification performance metric?

Accuracy, F1, or TPR (a.k.a recall or sensitivity) are well-known and widely used metrics for evaluating and comparing the performance of machine learning based classification.

But, are we sure we evaluate classifiers’ performance correctly? Are they or others such as BACC (Balanced Accuracy), CK (Cohen’s Kappa), and MCC (Matthews Correlation Coefficient) robust?

My latest research on benchmarking classification performance metrics (BenchMetrics) has just been published with SpringerNature in Neural Computing and Applications (SCI, Q1) journal.

Read here: https://rdcu.be/cvT7d

Highlights

• A benchmarking method is proposed for binary-classification performance metrics.
• Meta-metrics (metric about metric) and metric-space concepts are introduced.
• The method (BenchMetrics) tested 13 available and two recently proposed metrics.
• Critical issues are revealed in common metrics while MCC is the most robust one.
• Researchers should use MCC for performance evaluation, comparison, and reporting.

Abstract

This paper proposes a systematic benchmarking method called BenchMetrics to analyze and compare the robustness of binary-classification performance metrics based on the confusion matrix for a crisp classifier. BenchMetrics, introducing new concepts such as meta-metrics (metrics about metrics) and metric-space, has been tested on fifteen well-known metrics including Balanced Accuracy, Normalized Mutual Information, Cohen’s Kappa, and Matthews Correlation Coefficient (MCC), along with two recently proposed metrics, Optimized Precision and Index of Balanced Accuracy in the literature. The method formally presents a pseudo universal metric-space where all the permutations of confusion matrix elements yielding the same sample size are calculated. It evaluates the metrics and metric-spaces in a two-staged benchmark based on our proposed eighteen new criteria and finally ranks the metrics by aggregating the criteria results. The mathematical evaluation stage analyzes metrics’ equations, specific confusion matrix variations, and corresponding metric-spaces. The second stage, including seven novel meta-metrics, evaluates the robustness aspects of metric-spaces. We interpreted each benchmarking result and comparatively assessed the effectiveness of BenchMetrics with the limited comparison studies in the literature. The results of BenchMetrics have demonstrated that widely used metrics have significant robustness issues, and MCC is the most robust and recommended metric for binary-classification performance evaluation.

A critical question for the research community who wish to derive objective research outcomes

The chosen performance metric is the only instrument to determine which machine learning algorithm is the best.

So, for any specific classification problem domain in the literature:

Question: If we evaluate the performances of algorithms based on MCC will the comparisons and ranks change?

Answer: I think so. At least, we should try and see.

Question: But how?

Answer:

Please, share the results with me.

Citation for the article:

Canbek, G., Taskaya Temizel, T. & Sagiroglu, S. BenchMetrics: a systematic benchmarking method for binary classification performance metrics. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-021-06103-6

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Big Data Technology Importance in Human Resource Management

Big data in human resource management refers to the use of several data sources to evaluate and improve practices including recruitment, training and development, performance, compensation, and end-to-end business performance.

It has attracted the attention of human resource professionals who can analyze huge amounts of data to answer important questions regarding employee productivity, the impact of training on business performance, employee attrition, and much more. Using sophisticated HR software that gives robust data analytics professionals can make smarter and more accurate decisions.

In this article, let’s dive further and understand the role of big data technology in the field of HR, in today’s fast-paced world, is done. Where there is the need of analyzing massive quantities of different information.

Recruiting top talent for the firms is the primary task of HR departments. They are required to select candidates’ resumes and interview appropriate applicants till they get the right person they are looking for. Big data offers a broader platform for the recruitment process, which is the Internet.

By integrating recruitment with social networking, HR recruiters can find more information about the candidates, such as personal video pictures, living conditions, social relationships, ability, etc., so that the applicant’s image becomes more vivid and can recruit the right fit. Moreover, the candidates can learn more about the organization for which they will be giving an interview of the information and the recruitment process becomes more open and transparent.

Compensation is the most essential indicator, which attracts potential applicants, and getting a salary is one of the main objectives of employees to participate in the work. Traditional performance management systems often have more qualitative and less quantitative terms and compensation is out of touch with performance results.

Data analytics are solutions that identify meaningful patterns in a set of data. They are helpful to quantify the performance of a firm, product, operation, team, or even employees to support the business decisions. Data compilation to manipulation and statistical analysis are the core elements of big data analysis.

With the help of big data technology in performance management, professionals can record daily workload, the specific content of the work, and the task achievement of each employee. The professionals who have done talent management certification programs will have the advantage to receive better compensation. Sophisticated HR management software, which performs these operations enhances the work efficiency and also reduces enterprise investment in human capital. They are also useful for calculating salaries automatically to get better insights on performance standards.

Employers can gather health-related data on their employees. As a result, more attractive and beneficial packages can be created. The certified HR professionals will get to enjoy more perks too. It is crucial to note that the organizations must be transparent about what they’re doing to avoid legal concerns related to discrimination practices. This can be done by revealing how they are collecting and using this data.

Workforce training is an important part to enable the sustainable development of a business. Successful training can enhance employees’ level of knowledge and improve their performance. So that firms can keep their benefits of human resources in fierce competition and increase their profitability.

Traditional employee training needs a lot of manpower, material, and financial resources. With the advent of big data, information access and sharing have become more convenient. Employees can easily search and find the information they want to learn through the network at any time or anywhere. Workforce big data analysis – makes use of software to apply statistical models to employee-related data to optimize human resource management (HRM). It is also helpful to record data of studied behaviors of each employee, who not only can use the online system to analyze their own training needs but also can choose their favorite form of training.

The role of big data in human resource management has become more prominent. The value of data has certainly accelerated the way a business functions. This rapidly-growing technology enables HR professionals to effectively manage employees so that business goals can be accomplished more quickly and efficiently.

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Open AI Codex Challenge Seen By the Participants

On the 12th of August, Open AI hosted a hackathon for all those interested in trying out Codex. Codex is a new generation of their GPT-3 algorithm that can translate plain English commands into code.

We at Serokell thought it would be interesting to try this out: right now free access to the beta is accessible only to a small group of people. One of our teammates got access to it after being on the waiting list for over a year.

What was the format?  

The point of the challenge was to solve 5 small tasks that were the same for everyone to test the system. To be fair, they were quite simple – maybe because Codex can’t solve complex problems. To give an example, one of the tasks was to use pandas’ functionality to calculate the number of days between two dates in a string. There was a simple task dedicated to algorithms as well: for a binary tree it was needed to restore the original message. 

Our main motivation was to see what Codex can do, how well it understands tasks, and monitor the logic of its decisions. Spoiler alert: not everything was as great and smooth as during the Open AI demo!

What was the problem?

The first problem was connected with server lagging – maybe the company wasn’t ready for such a huge number of participants (a couple of thousands). Because of that, we wasted a lot of time trying to reconnect. Interestingly enough: the leaderboard had a weird logic. The solutions were rated by the time of completion, not by the time needed to solve the problem. So people who were late for the beginning of the challenge were apriori low on the scoreboard. 

To us, it seemed that Codex is not a very smart coder. First of all, it made quite many syntax mistakes. It can easily forget the closing bracket or introduce extra columns. Because of that, the code becomes incorrect. It really takes time and effort to catch these errors!

Secondly, it seems that Codex doesn’t know how to work with data types. You as a programmer have to be very careful, or the model will mess things up. 

For instance, in the previous example of a task that simply is counting days between dates, Codex messed up the sequence of actions for us. It forgot to convert string to date and tried to perform an operation with it as it is. 

Finally, the solutions that Codex proposes are not optimal. It’s a huge part of being a good programmer: to understand the task, break it down into realizable pieces and implement the most optimal solution in terms of execution time and memory. Codex does come up with some solutions but they’re far from being the most optimal ones. For example, when working with the tree, it wrote a while cycle instead of a for cycle and added extra conditions that weren’t in the initial task. Everyone knows that writing while loop instead of for loop is kind of a big no-no. 

Conclusion

All that said, it’s worth saying that Codex can’t be used as a no-code alternative to real programming. It’s unclear who Open AI is targeting with this solution. Non-programmers can’t use it, for the reasons mentioned above. Programmers would prefer to write code from scratch than sit and edit brackets in the Codex code. 

Before it was said that Codex will be behind the Copilot, the initiative was realized together with GitHub. But firstly, it doesn’t work as an autocomplete tool like PyCharm. The majority of the team doesn’t write code in GitHub and uses it simply for project management. So it’s unclear what Open AI is going to do with Codex.

Anyhow, it’s an interesting initiative that has the potential to greatly improve the more people use it. So perhaps in the future, it will become a super user-friendly alternative to no-code solutions for non-programmers.

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Scaling Data Science And AI To Boost Business Growth

Data science and AI has become a requirement for business growth. The technology has advanced enough to predict customer’s choices and satisfy their needs. 

The volume of data generated per day is predicted to reach 463 exabytes by 2025. On the Internet, the world spends about $1 million per minute on goods. This huge amount of data, known as big data, has increased the need for qualified data science workers. 

According to the US Bureau of Labor Statistics, the employment of data scientists is predicted to grow 15% by 2029, much faster than the 4% average for all occupations. Graduates with exceptional data management skills are preferred both in large corporations and small businesses.

Almost 79% of CEOs believe that implementing artificial intelligence in companies will make their jobs more efficient and easier. AI results in 50% more sales, up to 60% cost reductions, and more. 

Leveraging AI helps you to reach out to your target audience more efficiently, nurture their needs and increase revenue. For instance, you can improve your landing page relevancy and improve the ad quality score for increased conversions. 

Sales and Marketing

AI-powered apps may soon be able to manage all of your everyday tasks. AI-powered predictive content tools enable marketers to be more strategic while also reducing workload. Spending less time on less profitable possibilities and more time on your most profitable segments will allow your sales force to enhance its win rate, cover more ground, and eventually increase revenue.

AI-powered marketing applications crawl your website for blog articles, case studies, white papers, ebooks, videos, and other content. For a complete omnichannel approach, insights can be leveraged to engage visitors across email, online, social, and mobile channels. AI can assist with everything from managing your calendar to scheduling meetings to appraising a sales team’s pipeline by automatically executing these things for you or making them significantly easier by making recommendations based on your prior usage data. 

As a result, companies may now engage in one-to-one value marketing, which was previously impossible without significant scalability.  This enables the system to predict what each user wants to buy. AI knows what to market since it closely monitors customer behaviour. Salespeople devote hours to study and interviews in order to discover what AI already knows.

One such tool to ease your work is Finteza. It offers analytical solutions and the advertising engine. Analytics aids in the collection of data about your site, such as user behaviour, traffic quality, weak points, and inefficient advertising channels. The advertising engine allows for the management of online campaigns, the generation of banners, and the placement of targeted adverts all from a single interface. 

Accurate Customer Demand Prediction

Today, predictive analytics is all about linking disparate systems and data sets to conduct adequate analysis and extract meaningful information from seemingly chaotic data. Advanced analytics-powered solutions can significantly cut costs associated with failures, customer attrition, and other factors.

Data collection and analysis on a bigger scale can help you detect emerging trends in your market. Purchase data, celebrities and influencers, and search engine searches can all be used to determine which goods individuals are interested in. Clothing upcycling, for example, is becoming more popular as an environmentally conscientious approach to update one’s wardrobe. According to Nielson research, 81% of consumers strongly believe that businesses should assist in improving the environment.

Demand prediction is an important part of building a thriving business. If you take a look at a firm that is growing, it means that they have a growth strategy in place. In demand prediction, you calculate a firm’s past sales and then their potential growth rate. 

Data processing, mining, analysis, and manipulation assists in the demand forecasting of an organization. You can analyse the historical data of your competitors’ performance, the firm’s ROI or attrition rate and predict the future just with the help of AI and machine learning. 

Acxiom, IBM, Information Builders, Microsoft, SAP, SAS Institute, Tableau Software, and Teradata are among the major predictive analytics software and service companies.

By remaining updated on the behaviours of your target market, you can make business decisions that will put you ahead of the competition.

Final Thoughts

In business management, the trend is toward customer-centricity, personalization, and data-driven decision-making. Regardless of how organisations do this, being open and adaptable to change puts them one step closer to remaining competitive. Start leveraging the power of data science and AI to elevate your business growth. 

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The Growing Importance of Data and AI Literacy – Part 2

This is the second part of a 2-part series on the growing importance of teaching Data and AI literacy to our students.  This will be included in a module I am teaching at Menlo College but wanted to share the blog to help validate the content before presenting to my students.

In part 1 of this 2-part series “The Growing Importance of Data and AI Literacy”, I talked about data literacy, third-party data aggregators, data privacy, and how organizations monetize your personal data.  I started the blog with a discussion of Apple’s plans to introduce new iPhone software that uses artificial intelligence (AI) to detect and report child sexual abuse.  That action by Apple raises several personal privacy questions including:

• How much personal privacy is one willing to give up trying to halt this abhorrent behavior?
• How much do we trust the organization (Apple in this case) in their use of the data to stop child pornography?
• How much do we trust that the results of the analysis won’t get into unethical players’ hands and used for nefarious purposes?

In particular, let’s be sure that we have thoroughly vented the costs associated with the AI model’s False Positives (accusing an innocent person of child pornography) and False Negatives (missing people who are guilty of child pornography). That is the focus of Part 2!

AI literacy starts by understanding how an AI model works (See Figure 1).

Figure 1: “Why Utility Determination Is Critical to Defining AI Success“

AI models learn through the following process:

1. The AI Engineer (in very close collaboration with the business stakeholders) defines the AI Utility Function, which are the KPIs against which the AI model’s progress and success will be measured.
2. The AI model operates and interacts within its environment using the AI Utility Function to gain feedback in order to continuously learn and adapt its performance (using backpropagation and stochastic gradient descent to constantly tweak the models weights and biases).
3. The AI model seeks to make the “right” or optimal decisions, as framed by the AI Utility Function, as the AI model interacts with its environment.

Bottom-line: the AI model seeks to maximize “rewards” based upon the definitions of “value” as articulated in the AI Utility Function (Figure 3).

Figure 2:  “Will AI Force Humans to Become More Human?”

To create a rational AI model that understands how to make the appropriate decisions, the AI programmer must collaborate with a diverse cohort of stakeholders to define a wide range of sometimes-conflicting value dimensions that comprise the AI Utility Function.  For example, increase financial value, while reducing operational costs and risks, while improving customer satisfaction and likelihood to recommend, while improving societal value and quality of life, while reducing environmental impact and carbon footprint.

Defining the AI Utility Function is critical because as much credit as we want to give AI systems, they are basically dumb systems that will seek to optimize around the variables and metrics (the AI Utility Function) that are given to them.

To summarize, the AI model’s competence to take “intelligent” actions is based upon “value” as defined by the AI Utility Function.

One of the biggest challenges in AI Ethics has nothing to do with the AI technology and has everything to do with Confirmation Bias.  AI model Confirmation Bias is the tendency for an AI model to identify, interpret, and present recommendations in a way that confirms or supports the AI model’s preexisting assumptions. AI model confirmation bias feeds upon itself, creating an echo chamber effect with respect to the biased data that continuously feeds the AI models. As a result, the AI model continues to target the same customers and the same activities thereby continuously reinforcing preexisting AI model biases.

As discussed in Cathy O’Neil’s book “Weapons of Math Destruction”, the confirmation biases built into many of the AI models that are used to approve loans, hire job applicants, and accept university admissions are yielding unintended consequences that severely impact individuals and society. Creating AI models that overcome confirmation biases takes upfront work and some creativity.  That work starts by 1) understanding the costs associated with the AI model’s False Positives and False Negatives and 2) building a feedback loop (instrumenting) where the AI model is continuously-learning and adapting from tis False Positives and False Negatives.

Instrumenting and measuring False Positives – the job applicant you should not have hired, the student you should not have admitted, the consumer you should not have given the loan – are fairly easy because there are operational systems to identify and understand the ramifications of those decisions.  The challenge is identifying the ramifications of the False Negatives.

“In order to create AI models that can overcome model bias, organizations must address the False Negatives measurement challenge. Organizations must be able to 1) track and measure False Negatives to 2) facilitate the continuously-learning and adapting AI models that mitigates AI model biases.”

The instrumenting and measuring of False Negatives – the job applicants you did not hire, the student applicant you did not admit, the customer to whom you did not grant a loan – is hard, but possible.  Think about how an AI model learns – you label the decision outcomes (success or failure), and the AI model continuously adjusts the variables that are predictors of those outcomes.  If you don’t feedback to the AI model its False Positives and False Negatives, then the model never learns, never adapts, and in the long run misses market opportunities. See my blog “Ethical AI, Monetizing False Negatives and Growing Total Address” for more details.

Organizations need a guide for creating AI models that are transparent, unbiased, continuously-learn and adapt, and exist in support of societal laws and norms.  That’s the role of the Ethical AI Application Pyramid (Figure 3).

Figure 3: The Ethical AI Application Pyramid

The Ethical AI Application Pyramid embraces the aspirations of Responsible AI by ensuring the ethical, transparent, and accountable application of AI models in a manner consistent with user expectations, organizational values and societal laws and norms.  See my blog “The Ethical AI Application Pyramid” for more details about the Ethical AI Application Pyramid.

AI run amuck is a favorite movie topic.  Let’s review a few of them (each of these is on my rewatchable list):

• Eagle Eye: An AI super brain (ARIIA) uses Big Data and IOT to nefariously influence humans’ decisions and actions.
• I, Robot: Way cool looking autonomous robots continuously learn and evolve empowered by a cloud-based AI overlord (VIKI).
• The Terminator: An autonomous human killing machine stays true to its AI Utility Function in seeking out and killing a specific human target, no matter the unintended consequences.
• Colossus: The Forbin Project: An American AI supercomputer learns to collaborate with a Russian AI supercomputer to protect humans from killing themselves, much to the chagrin of humans who are intent on killing themselves.
• War Games: The WOPR (War Operation Plan Response) AI system learns through game playing that the only smart nuclear war strategy is “not to play”.
• 2001: The AI-powered HAL supercomputer optimizes its AI Utility Function to accomplish its prime directive, again no matter the unintended consequences.

There are some common patterns in these movies – that AI models will seek to optimize their AI Utility Function (their prime directive) no matter the unintended consequences. 

But here’s the real-world AI challenge: the AI models will not be perfect out of the box.  The AI models, and their human counterparts, will need time to learn and adapt. Will people be patient enough to allow the AI models to learn? And do normal folks understand that AI models are never 100% accurate and while they will improve over time with more data, models can also drift over time as the environment in which they are working changes? And are we building “transparent” models so folks can understand the rationale behind the recommendations that AI models make?

These questions are the reason why Data and AI literacy education must be a top priority if AI is going to reach its potential…without the unintended consequences…

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Opportunities and Risks of Foundation Models

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