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AI is here and will be an increasingly prominent tool for many companies, big and small. For those working in financial positions, AI will take over manual data entry tasks and do financial forecasting using predictive analytics. AI will help you discover patterns in your financial data that you can use to take action. Your auditors will use an AI tool to compare transactions from prior periods to look for data irregularities. A finance person might first be introduced to machine learning by adopting an intelligent automation solution using a bot to vouch for supplier invoices in accounts payable. At first, bots will be able to take over tasks that can be easily automated. In the not-so-distant future, people will have coworking relationships with AI applications.

The following workforce skills will help you stay valuable in the era of AI:

1. Complex problem solving: Technology will solve our simple problems. I saw early career success by proposing solutions for company problems. I hire people who put a high emphasis on problem-solving. AI will support human problem-solving efforts.
2. Creativity: This helps employees find solutions when problem-solving.
3. Openness to change: People will need to adapt to change, especially as technology advances at an ever faster pace.
4. Collaboration: Working with other departments will require collaboration. This may be done by working with AI, as well as by working with people.
5. Emotional intelligence: This is becoming a more recognized skill. Employees with high emotional intelligence use empathy and have an understanding of the emotional dynamics of a team. AI will have difficulty with this skill.
6. Enthusiasm for diversity: Diversity needs to be authentic inclusion. It is not a checkbox but the recognition that your team will be stronger with various life experiences, cultural norms and experiences, thinking patterns, and approaches to problem-solving.
7. Empathy: This is a key servant leadership principle, and it helps people cope with difficult situations and contributes to the concept of fairness. AI will not likely have the ability to empathize anytime soon.
8. Good judgment: AI will be programmed by humans. You want employees to have good judgment to help guide ethical behavior of AI. Good judgment is a broad term that applies to many areas.
9. Integrity: Integrity means being honest and having a strong moral character. As technology advances, there will be greater ethical challenges to come, and this skill helps prevent dishonest behavior in the workplace.
10. People management: This is a critical skill as people are the heart of a company and good managers have an active interest in people. It is a talent that can be lacking in people who get promoted into management. AI will lack the ability to manage people effectively.

There are certainly more than 10 skills that complement AI, but this is a good list to get you to start preparing for the future. At this point, AI is really an advanced calculator that is dependent on lots of data to provide value. It will not easily acquire the skills listed above, and this is where humans provide value in the human-AI partnership.

The original article can be found here.

To support for AI technology and development for human-centric values, Michael Dukakis Institute for Leadership and Innovation (MDI) and Artificial Intelligence World Society (AIWS.net) has developed AIWS Ethics and Practice Index to measure the ethical values and help people achieve well-being and happiness, as well as solve important issues, such as SDGs. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.

Multicore chip making bought us two decades of performance gains, but now it’s reaching the end of its useful life.

Regardless of how “multi” they are, traditional CPU cores are not nearly efficient enough at running the massive data models researchers are now building, especially in intensive, artificial intelligence (AI)-enabled applications like natural language processing (NLP), image processing and recommender systems.

We need a new architecture better suited to current and future artificial intelligence and machine learning applications — and we need it now.

AI is steadily but surely eating software. ML applications are developed to achieve outcomes that are probabilistic rather than deterministic. They don’t need the nth degree of high precision — they need high-enough probability certainty to determine that an image of a car is indeed a car. The reduced need for precision means far more of a chip’s transistors can be used for the important work of computation rather than keeping multiple cores in sync.

High-performance, flexible solutions will be necessary for continual disruption and an important democratizing factor, allowing unexpected underdogs to become the AI heroes of the future without having to go head-to-head with a FAANG or tech-savvy nation-state. Multicore kept the industry afloat for many years, and it will still be important in many applications. But for a future dominated by compute-hungry, probabilistic, AI-powered applications, we’ll need some truly clever solutions. RDA is one answer, and chances are we’ll need even more.

The original article can be found here.

According to Artificial Intelligence World Society Innovation Network (AIWS.net), AI can be an important tool tfor helping people achieve well-being and happiness, relieve them of resource constraints and arbitrary/inflexible rules and processes. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.

Humans have many kinds of biases. To name just a few, we suffer from confirmation bias, which means that we tend to focus on information that confirms our preconceptions about a topic; from anchoring bias, where we make decisions mostly relying on the first piece of information we receive on that subject; and from gender bias, where we tend to associate women with certain traits, activities, or professions, and men with others. When we make decisions, these types of biases often creep in unconsciously, resulting in decisions that are ultimately unfair and unobjective.

These same types of bias can show up in artificial intelligence (AI), especially when using machine learning techniques to program an AI system. A commonly-used technique called “supervised machine learning” requires that AI systems be trained with a large number of examples of problems and solutions. For example, if we want to build an AI system that can decide when to accept or reject a loan application, we would train it with many examples of loan applications, and for each application, we would give it the correct decision (either accept or reject the application).

The AI system would find useful correlations in such examples and use them to make (hopefully correct) decisions on new loan applications. After the training phase, a test phase on another set of examples checks that the system is accurate enough and ready for deployment. However, if the training dataset is not balanced, inclusive, or representative enough of the dimensions of the problem we want to solve, the AI system may become biased. For example, if all accepted loan applications in the training dataset are related to men and all rejected ones are related to women, then the system will pick up the correlation between gender and acceptability as a form of bias and will use this bias when making decisions on new applications going forward.

The original article can be found here.

To support for AI Ethics, Michael Dukakis Institute for Leadership and Innovation (MDI) and Artificial Intelligence World Society (AIWS.net) has developed AIWS Ethics and Practice Index to measure the ethical values and help people achieve well-being and happiness, as well as solve important issues, such as SDGs. Regarding to AI Ethics, AI World Society (AIWS.net) initiated and promoted to design AIWS Ethics framework within four components including transparency, regulation, promotion and implementation for constructive use of AI. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.