Artificial Intelligence

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Gender Identity for AI

Artificial neural networks have given AIs the functionality for complex problem solving and pattern recognition, and they have entered the workforce, particularly in areas of big data analysis and global finance. As we begin to interact with and study these new learning machines, interesting questions arise. Are they going to take on human behavioral and gender distinctions (gender identity), because they have been programmed with data sets that have unconscious bias? Will those who are giving the learning machines feedback to focus their problem solving allow behavioral constraints into the teaching? If we give the AIs a woman’s voice, and a woman’s name, will we interact with her as if she was a woman? And does that mean she will in turn internalize those social expectations and become more female?

Naturally we are interested in all things having to do with gender. It is the first sentence the world places upon us, when the midwife announces boy or girl. We love gender. We give our teddy bears genders, and can describe in detail why we think-no, why we know that our little darling is a boy or girl. We give our cars genders, names, and personalities. It’s just because we’re human, and we want to humanize the things we love, and that surround us. And part of humanizing inanimate objects is to give them a name, a gender, and shower them with affection.

Part of our fascination with gender has led to some poor science, the popularity of which has trickled down into our collective consciousness. The idea that male brains and female brains are different in a significant way is probably not true, though the debate rages. Structure follows function, and hormones affect the developing brain. But even with minor structural and functional differences in the brains that are most probably hormonally-based, there is very little difference in boys and girl’s brains. There is a much wider variance between individuals than can be measured than between generalized groups based just on gender. We are more complicated than can be described in pop-science about hardwired aggression and nurture vs nature.

What is different between genders is communication, how we use language, and there the gender differences are significant enough to be measured. If we think of communication as the way we input data into our brains, we grow our biological neural networks with the complex range of human communication to which we’re exposed. And there are differences between male and female communication.

So with the science showing that biological neural networks- aka human brains- are more complex than can be measured, but are influenced by hormones, language, biology, and the wide range of human culture, we are left to consider if artificial neural networks will also be influenced by language and human culture. (This is assuming that the artificial neural networks that are biology and hormonally mediated are still a few years in the future.) Continue reading

lawyers

Insurers and Lawyers are stepping into AI

The insurance business revolves around measuring and pricing risks. In short, it’s a business about caution. Perhaps that’s the reason why insurance companies have lagged other industries, such as banking, in adopting new technologies that offer more powerful analytics capabilities. But the insurance industry’s reluctance to adopt new technologies is breaking down. So-called insurtech is one of the industries drawing investor interest as startup companies test and market new software solutions, Coin Journal reported, citing a report from CB Insights and KPMG. Insurance companies are interested in using Internet-of-Things technologies to identify and mitigate risk, while also incorporating other technologies that identify fraud, improve efficiency, and cut costs.

Financial services quickly embraced new technology because its use in improving profits was readily apparent to bankers and investors. Gathering information in real-time and timing transactions based on the steady flow of information helps financial industry players make money. In the insurance field, the name of the game is saving money. New technology can help insurance companies more accurately price policies to risk and while also cutting down on fraudulent claims that are costly to the industry. Daily Fintech notes that one of the more transformative developments in insurtech is the emergence of telematics. The capability to gather and transmit near-real time information produces even more data points from which an insurer can more precisely make their risk assessments. These bountiful data sources produce a tremendous amount of data from the home for home insurance, and from the car for property and casualty insurance, Daily Fintech explains.

The collection and analysis of data is not limited to the home and the car. Wearable technology first found its place in people’s lives in fitness applications. But these technologies have matured to a point where they can be used for healthcare applications. The ability to monitor people and collect data of a person over a longer period of time yields measurable data that insurance companies can use to assessing health risks for life and health insurance, Daily Fintech says. Continue reading

predictive analytics

Better Planning with Predictive Analytics

In industries with longstandig customer relationships, you want to know how customer-churns are impacting the customer value. The better you can analyse the future churns within or outside your company, the better you can react to these changes. Typical industries for this analysis are: Telecommunications, Banking, etc. The ability to access and analyze data from many cultures and geographic regions is one way the current machine learning platforms can produce models that deal with global human behavior. In the past, when we were dependent on subject matter experts, predictions were often limited by geography and culture.
Predictive analytics will help you to make more meaningful analysis with Big Data and allows you to make forward-looking business decisions. Before starting a predictive analysis, you need to know the components of your value drivers in details. E.g. the churn rate is one important value driver and the financial contribution margin analysis includes the following categories:

Net Revenues
-Direct Costs
Contribution Margin 1
-Acquisition and Retention costs
Contribution Margin 2
-Network Costs
Contribution Margin 3

Customer segmentation and churn modeling are important underlying tasks in order to make relevant predictions in the telcommunication sector. See example:
• Churn within your company (within price plans): In order to predict the churn-likeliness of customers you need to take into consideration the Social Data and Usage Data of your customers. The usage data includes trends of the following pricing components: Voice-Call-Patterns, Data-Usage, Roaming, Wholesale, etc. New market-developments like new handset models need also to be incorporated in your analysis. Social Data includes social network data (comments, recommendations) of your customers. Both (social and usage data) need to be merged and analysed (time line, clusters, regression, etc.).
• Churn outside your company: To get a better accuracy of churns outside your company you need to include the pricing developments of your competitors in your analysis.

Trying to measure the success of a group of company based on a limited number of KPI will not lead to a fair, motivational decision. It will lead to an oversimplified judgement of a situation and to shortsighted conculsions.
What can be done in order to improve a decision making process of a group:
• Use as many data sources as you have (big data, data lake)
• Use external data and research data – to bring the external view in the decision making process
• Find patterns relevant for your group or company – the patterns are different for all companies (you can’t guide companies with the same five KPI).
• Look at trends and personal feedback – build a story around the data
• Use IT extensively to measure and analyse data.
• Do not measure the KPI in a traditional financial form – the new KPI is explained in multidimensional ways.
• Automated processes are welcome, but be aware, that for an holistic picture you need good human analysts.
With today’s tools you can evaluate complexity much better than years ago. It’s possible to make better decisions in a more sophisticated world. Predictive Analysis helps you bringing your Business Intelligence to the next (analysis-)level and predicts how your customers are going to react in your price plans in the near future.

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predictive analysis

Predictive Analysis and human behaviour

Predictive Analytics is the branch of machine learning that is putting all the data to work. It takes large data sets and uses mathematical algorithms to form predictive models. Then statistical methods such as regression analysis are used to find the variables that influence the models. Finally, machine learning platforms use those predictive models to find patterns in the past that can allow predictions of patterns in the future.

Human behavior can be seen as a series of patterns that repeat, both individually and as a group, over time. This statistical fact doesn’t negate the possibility of free will; it allows us the use of our free will to repeat patterns of behavior that are most comfortable to us, considering the social, cultural, and family pressures that also influence us.

The analysis of patterns of behavior in humans as a group has been the work of historians, who can look back at great sweeps of time and see patterns that repeat. With the amount of data being collected now through online interactions and geotracking tools such as GPS, machine learning platforms are engaged in how to mine that huge amount of data for the very specific data needed to answer questions.

Predicting the future has been an art in which intuition based on expert knowledge and experience was used to make a predictive analysis. Those who are considered masters in their work combine experience with knowledge, and can see patterns from the past and predict patterns into the future. But we are constrained by the depth and breadth of experience and knowledge we can acquire; we are further constrained by unconscious bias and other human attributes. Continue reading

stratecta

New technologies for improving healthcare

Various thought leaders have opined that we are in the Fourth Industrial Revolution, as seen by the fact that new technologies are disrupting all industries, disciplines, and economies. By 2020, the digital universe will be 44 trillion gigabytes. This amount is doubling every two years.  Big Data will become so large that artificial intelligence (AI) will make sense of it for us. Already Google has launched the Google Deepmind Health project to scrutinize the data of patients’ medical records and provide better and faster service.

Mitchell Weiss, a robotics safety expert, identified the top three trends impacting occupational health and safety in 2017. They involve complexity of automation, collaborative automation, and complexity of user interface. Along with this there is an increase use of Big Data, artificial intelligence, and IIoT (Industrial Internet of Things) to do medical work.

Internet of Things (IoT) — the ability to connect any device to the Internet through an on and off switch — is a major component of telemedicine, which allows healthcare professionals to communicate with people long distance and provide consultation, diagnosis and treatment of various medical conditions. IOT Telemedicine, which has been gaining in popularity, is now expanding globally.

A Brief Primer on the Internet of Things

Internet of Things is a concept that can apply to things like washing machines, coffee makers, headphones, even parts of machines. The research and advisory firm Gartner estimates  that by 2020, more than 26 billion devices will be connected to IoT. Some analysts say the figure could go as high as 100 billion. In only a short time, our society will be a network of connected “things.” And these “things” include the robots and other devices that are connected to the Internet and can therefore consult with physicians and patients thousands of miles away.

Internet of Things in Healthcare Continue reading

Bell Curve

The Social Credit Score and the Bell Curve

The Social Credit Score is a system that China has had in trials for several years, and that uses the principles of credit scoring- data streams from several specific sources- to formulate a predictive score. The current credit score uses data from the past to predict future behavior, and allows financial institutions to evaluate risk. The social credit score is taking this model and enlarging it to fields of interest beyond financial behavior.

Some data sources are going to provide information that has a better predictive value for future behavior than others. And while humans often surprise their families and themselves by going off the rails, some patterns of behavior are bound to be repeated. The social credit score attempts to find the behaviors with the best predictive value, and use these values to determine how well a person functions in society.

With a large population, concerns of governments are the needs of the population. And population dynamics are different from tribal, family, or individual dynamics. As the world population grows and human society becomes more complex, we will be facing new challenges. We will be living in significantly denser social groups, for instance. Policy decisions will be made for the good of the entire group, and it is believed by those making these plans and decisions that the populations as a whole will be best served if everyone toes the line.

Toe the line. Follow the rules. Do what you are supposed to do. If you screw up, it goes on your permanent record. Very permanent. People can check your score. Employers, landlords, parents of the person you want to marry. Continue reading