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Today, the role of artificial intelligence in an enterprise has become so important that it has touched every facet of business. Its role will become more critical in the years to come.

For the purposes of this article, let us define A.I. as follows. Human intelligence is learnt from experience. Machines so far have been primarily used to follow instructions, i.e. programmed, hence machines have provided automation based on rules.

A.I. is not programmed to follow rules, it is like human intelligence, learns from “experience”, i.e. data.

A.I. application in businesses today can be divided into 5 key areas:

1. A.I. in data cleansing and streamlining.
2. A.I. in BI—i.e. AI to replace business analysts in preliminary analysis on dashboards.
3. A.I. in cognitive intelligence such as voice recognition, video analytics, face recognition.
4. A.I. in natural interaction – chat bots, NLP, Natural-Language Generation (NLG).
5. A.I. in expert systems—learning from myriad data sets and crystallizing an insight or action. This can be applied in classification of future unknowns e.g. fraud prevention, preventive maintenance. This can be applied also in forecasting quantities e.g. demand forecasting, supply shortage prediction. And can be applied in real time dynamic operations e.g. self-driving cars, dynamic digital marketing.

The original article was posted at Fortune India.

In support of positive AI development for human centric in the society, the Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) established the Artificial Intelligence World Society (AIWS.net) in 2018. According to AIWS.net, AI can be an important tool for 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.

The Internet of Things (IoT) is a technology helping us to reimagine daily life, but artificial intelligence (AI) is the real driving force behind the IoT’s full potential.

From its most basic applications of tracking our fitness levels, to its wide-reaching potential across industries and urban planning, the growing partnership between AI and the IoT means that a smarter future could occur sooner than we think.

There are four major segments in which the AIoT is making an impact: wearables, smart home, smart city, and smart industry:

1. Wearables

Wearable devices such as smartwatches continuously monitor and track user preferences and habits. Not only has this led to impactful applications in the healthtech sector, it also works well for sports and fitness. According to leading tech research firm Gartner, the global wearable device market is estimated to see more than $87 billion in revenue by 2023.

2. Smart Home

Houses that respond to your every request are no longer restricted to science fiction. Smart homes are able to leverage appliances, lighting, electronic devices and more, learning a homeowner’s habits and developing automated “support.”

This seamless access also brings about additional perks of improved energy efficiency. As a result, the smart home market could see a compound annual growth rate (CAGR) of 25% between 2020-2025, to reach $246 billion.

3. Smart City

As more and more people flock from rural to urban areas, cities are evolving into safer, more convenient places to live. Smart city innovations are keeping pace, with investments going towards improving public safety, transport, and energy efficiency.

The practical applications of AI in traffic control are already becoming clear. In New Delhi, home to some of the world’s most traffic-congested roads, an Intelligent Transport Management System (ITMS) is in use to make ‘real time dynamic decisions on traffic flows’.

4. Smart Industry

Last but not least, industries from manufacturing to mining rely on digital transformation to become more efficient and reduce human error.

From real-time data analytics to supply-chain sensors, smart devices help prevent costly errors in industry. In fact, Gartner also estimates that over 80% of enterprise IoT projects will incorporate AI by 2022.

The original article was posted at the World Economic Forum.

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.

Understanding and generalization beyond the training distribution are regarded as huge challenges in modern machine learning (ML) — and Yoshua Bengio argues it’s time to look at causal learning for possible solutions. In the paper Towards Causal Representation Learning, Turing Award honoree Bengio and his research team make an effort to unite causality and ML research approaches, delineate some implications of causality for ML, and propose critical areas for future research.

Bengio outlined the challenge in a causal representation learning talk he gave late last year, “I would say there are pretty significant gaps between current state-of-the-art in machine learning-driven AI and the intelligence that we see deployed in humans and many animals… We don’t have AI systems that actually understand at the level that humans do, or anywhere close.” Bengio characterized the meaning of human-level AI “understanding” as: capture causality; capture how the world works; understand abstract actions and how to use them to control; reason and plan, even in novel scenarios; explain what happened (inference, credit assignment); and generate out-of-distribution.

In this regard, most modern ML models remain far from true understanding, as they work only under fixed experimental conditions and interventions in the real world are seen as a nuisance that can hopefully be engineered away. It is therefore not surprising that most of today’s ML models lack an out-of-distribution generalization ability.

Causal learning, on the other hand, focuses on representing structural knowledge about the data-generating process to allow interventions and changes, making it easier to re-use and re-purpose learned knowledge. This approach is considered closer to human thinking.

This article was originally published at Synced.

Regarding to AI and Causal Inference, Professor Judea Pearl is a distinguished pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl won the Turing Award. In 2020, Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) also awarded Professor Pearl as World Leader in AI World Society (AIWS). At this moment, Professor Judea is a Mentor of AIWS.net and Head of Modern Causal Inference section, which is one of important AIWS.net.

In computer science, the main outlets for peer-reviewed research are not journals but conferences, where accepted papers are presented in the form of talks or posters. In June, 2019, at a large artificial-intelligence conference in Long Beach, California, called Computer Vision and Pattern Recognition, I stopped to look at a poster for a project called Speech2Face. Using machine learning, researchers had developed an algorithm that generated images of faces from recordings of speech. A neat idea, I thought, but one with unimpressive results: at best, the faces matched the speakers’ sex, age, and ethnicity—attributes that a casual listener might guess. That December, I saw a similar poster at another large A.I. conference, Neural Information Processing Systems (Neurips), in Vancouver, Canada.

Many kinds of researchers—biologists, psychologists, anthropologists, and so on—encounter checkpoints at which they are asked about the ethics of their research. This doesn’t happen as much in computer science. Funding agencies might inquire about a project’s potential applications, but not its risks. University research that involves human subjects is typically scrutinized by an I.R.B., but most computer science doesn’t rely on people in the same way. In any case, the Department of Health and Human Services explicitly asks I.R.B.s not to evaluate the “possible long-range effects of applying knowledge gained in the research,” lest approval processes get bogged down in political debate. At journals, peer reviewers are expected to look out for methodological issues, such as plagiarism and conflicts of interest; they haven’t traditionally been called upon to consider how a new invention might rend the social fabric.

A few years ago, a number of A.I.-research organizations began to develop systems for addressing ethical impact. The Association for Computing Machinery’s Special Interest Group on Computer-Human Interaction (sigchi) is, by virtue of its focus, already committed to thinking about the role that technology plays in people’s lives; in 2016, it launched a small working group that grew into a research-ethics committee. The committee offers to review papers submitted to sigchi conferences, at the request of program chairs. In 2019, it received ten inquiries, mostly addressing research methods: How much should crowd-workers be paid? Is it O.K. to use data sets that are released when Web sites are hacked? By the next year, though, it was hearing from researchers with broader concerns. “Increasingly, we do see, especially in the A.I. space, more and more questions of, Should this kind of research even be a thing?” Katie Shilton, an information scientist at the University of Maryland and the chair of the committee, told me.

Shilton explained that questions about possible impacts tend to fall into one of four categories. First, she said, “there are the kinds of A.I. that could easily be weaponized against populations”—facial recognition, location tracking, surveillance, and so on. Second, there are technologies, such as Speech2Face, that may “harden people into categories that don’t fit well,” such as gender or sexual orientation. Third, there is automated-weapons research. And fourth, there are tools “to create alternate sets of reality”—fake news, voices, or images.

The original article was published at The New Yorker.

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.

These days, every business is a software business. As companies try to keep up with the rush to create new software, push updates, and test code along the way, many are realizing that they don’t have the manpower to keep pace, and that new developers can be hard to find. But, many don’t realize that it’s possible to do more with the staff they have, making use of new advances in AI and automation. AI can be used to address bugs and help write code, but it’s greatest time saving opportunity may be in unit testing, in which each unit of code it checked — tedious, time-consuming work. Using automation here can free up developers to do other (more profitable) work, but it can also allow companies to test more expansively and thoroughly than they would have before, addressing millions of lines of code — including legacy systems that have been built on — that may have been overlooked.

Not all of the software development workflow can be automated, but gradual improvements in technology have made it possible to automate increasingly significant tasks: Twenty years ago, a developer at SUN Microsystems created an automated system — eventually named Jenkins — that removed many of the bottlenecks in the continuous integration and continuous delivery software pipeline. Three years ago, Facebook rolled out a tool called Getafix, which learns from engineers’ past code repairs to recommend bug fixes. Ultimately these advances — which save developers significant time — will limit failures and downtimes and ensure reliability and resilience, which can directly impact revenue.

But as AI speeds up the creation of software, the amount of code that needs to be tested is piling up faster than developers can effectively maintain. Luckily, automation — and new automated tools — can help with this, too.

Automation is coming to all parts of the software development process, some sooner than others — as AI systems become increasingly powerful, the options for automation will only grow. OpenAI’s massive language model, GPT-3, can already be used to translate natural human language into web page designs and may eventually be used to automate coding tasks. But eventually, large portions of the software construction, delivery, and maintenance supply chain are going to be handled by machines. AI will, in time, automate the writing of application software altogether.

The original article was published at the Harvard Business Review.

In support of positive AI development for the society, Michael Dukakis Institute for Leadership and Innovation (MDI) and Boston Global Forum (BGF) has established Artificial Intelligence World Society Innovation Network (AIWS.net). In this effort, MDI and BGF invite participation and collaboration with governments, 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. This initiative is to develop positive AI for helping people achieve well-being and happiness, relieve them of resource constraints and arbitrary/inflexible rules and processes, and solve important issues, such as SDGs.