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What’s a Trend?

When a topic is quickly growing in popularity, it is often said to be trending, but there are many different trends or patterns we might find in this data, including historical trends. These patterns might help us to identify, understand, and predict how our world is changing.

Using Google Trends

... is a tool that allows you to visualize data about search history across different times and locations. You can  look for interesting patterns, trends, or relationships between multiple trends and try to tell the story that pattern is showing.

Access it here: https://www.google.com/trends/

Find the Explore text box or go to the Explore section of the site https://www.google.com/trends/explore

Do your first comparison of trends  You can enter any two or three things you like. This example compares streaming, mp3, and cd.

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Weather forecasting

Ebola outbreak model

Puzzle game about folding proteins

Social distancing simulation of sneeze/cough

Coronavirus simulations

Mapping simulations for eyecare

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is the process to turn raw data into useful information. This process uses software to look for patterns in large batches of data, and can be used by businesses to learn more about their customers. It can be used to increase sales, decrease costs and develop marketing strategies.

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History of AI from... Alan Turings mathematical posit that  machines, like humans, could use available information as well as reasoning to solve problems and make decisions, ...until now with driverless cars and apps that help visually impaired people detect objects.

Video Link
Ethics of AI

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Can Machines Think?

In the first half of the 20 th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the "heartless" Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why can't machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.

Making the Pursuit Possible

Unfortunately, talk is cheap. What stopped Turing from getting to work right then and there? First, computers needed to fundamentally change. Before 1949 computers lacked a key prerequisite for intelligence: they couldn't store commands, only execute them. In other words, computers could be told what to do but couldn't remember what they did. Second, computing was extremely expensive. In the early 1950s, the cost of leasing a computer ran up to $200,000 a month. Only prestigious universities and big technology companies could afford to dillydally in these uncharted waters. A proof of concept as well as advocacy from high profile people were needed to persuade funding sources that machine intelligence was worth pursuing.

The Conference that Started it All

Five years later, the proof of concept was initialized through Allen Newell, Cliff Shaw, and Herbert Simon's, Logic Theorist. The Logic Theorist was a program designed to mimic the problem solving skills of a human and was funded by Research and Development (RAND) Corporation. It's considered by many to be the first artificial intelligence program and was presented at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in 1956. In this historic conference, McCarthy, imagining a great collaborative effort, brought together top researchers from various fields for an open ended discussion on artificial intelligence, the term which he coined at the very event. Sadly, the conference fell short of McCarthy's expectations; people came and went as they pleased, and there was failure to agree on standard methods for the field. Despite this, everyone whole-heartedly aligned with the sentiment that AI was achievable. The significance of this event cannot be undermined as it catalyzed the next twenty years of AI research.

Roller Coaster of Success and Setbacks

From 1957 to 1974, AI flourished. Computers could store more information and became faster, cheaper, and more accessible. Machine learning algorithms also improved and people got better at knowing which algorithm to apply to their problem. Early demonstrations such as Newell and Simon's General Problem Solver and Joseph Weizenbaum's showed promise toward the goals of problem solving and the interpretation of spoken language respectively. These successes, as well as the advocacy of leading researchers (namely the attendees of the DSRPAI) convinced government agencies such as the Defense Advanced Research Projects Agency (DARPA) to fund AI research at several institutions. The government was particularly interested in a machine that could transcribe and translate spoken language as well as high throughput data processing. Optimism was high and expectations were even higher. In 1970 Marvin Minsky told Life Magazine, "from three to eight years we will have a machine with the general intelligence of an average human being." However, while the basic proof of principle was there, there was still a long way to go before the end goals of natural language processing, abstract thinking, and self-recognition could be achieved.

Breaching the initial fog of AI revealed a mountain of obstacles. The biggest was the lack of computational power to do anything substantial: computers simply couldn't store enough information or process it fast enough. In order to communicate, for example, one needs to know the meanings of many words and understand them in many combinations. Hans Moravec, a doctoral student of McCarthy at the time, stated that "computers were still millions of times too weak to exhibit intelligence." As patience dwindled so did the funding, and research came to a slow roll for ten years.

In the 1980's, AI was reignited by two sources: an expansion of the algorithmic toolkit, and a boost of funds. John Hopfield and David Rumelhart popularized "deep learning" techniques which allowed computers to learn using experience. On the other hand Edward Feigenbaum introduced expert systems which mimicked the decision making process of a human expert. The program would ask an expert in a field how to respond in a given situation, and once this was learned for virtually every situation, non-experts could receive advice from that program. Expert systems were widely used in industries. The Japanese government heavily funded expert systems and other AI related endeavors as part of their Fifth Generation Computer Project (FGCP). From 1982-1990, they invested $400 million dollars with the goals of revolutionizing computer processing, implementing logic programming, and improving artificial intelligence. Unfortunately, most of the ambitious goals were not met. However, it could be argued that the indirect effects of the FGCP inspired a talented young generation of engineers and scientists. Regardless, funding of the FGCP ceased, and AI fell out of the limelight.

Ironically, in the absence of government funding and public hype, AI thrived. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBM's Deep Blue, a chess playing computer program. This highly publicized match was the first time a reigning world chess champion loss to a computer and served as a huge step towards an artificially intelligent decision making program. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows. This was another great step forward but in the direction of the spoken language interpretation endeavor. It seemed that there wasn't a problem machines couldn't handle. Even human emotion was fair game as evidenced by Kismet, a robot developed by Cynthia Breazeal that could recognize and display emotions.

Time Heals all Wounds

We haven't gotten any smarter about how we are coding artificial intelligence, so what changed? It turns out, the fundamental limit of computer storage that was holding us back 30 years ago was no longer a problem. Moore's Law, which estimates that the memory and speed of computers doubles every year, had finally caught up and in many cases, surpassed our needs. This is precisely how Deep Blue was able to defeat Gary Kasparov in 1997, and how Google's was able to defeat Chinese Go champion, Ke Jie, only a few months ago. It offers a bit of an explanation to the roller coaster of AI research; we saturate the capabilities of AI to the level of our current computational power (computer storage and processing speed), and then wait for Moore's Law to catch up again.

Artificial Intelligence is Everywhere

We now live in the age of " big data," an age in which we have the capacity to collect huge sums of information too cumbersome for a person to process. The application of artificial intelligence in this regard has already been quite fruitful in several industries such as technology, banking, marketing, and entertainment. We've seen that even if algorithms don't improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. There may be evidence that Moore's law is slowing down a tad, but the increase in data certainly hasn't lost any momentum. Breakthroughs in computer science, mathematics, or neuroscience all serve as potential outs through the ceiling of Moore's Law.

The Future

So what is in store for the future? In the immediate future, AI language is looking like the next big thing. In fact, it's already underway. I can't remember the last time I called a company and directly spoke with a human. These days, machines are even calling me! One could imagine interacting with an expert system in a fluid conversation, or having a conversation in two different languages being translated in real time. We can also expect to see driverless cars on the road in the next twenty years (and that is conservative). In the long term, the goal is general intelligence, that is a machine that surpasses human cognitive abilities in all tasks. This is along the lines of the sentient robot we are used to seeing in movies. To me, it seems inconceivable that this would be accomplished in the next 50 years. Even if the capability is there, the ethical questions would serve as a strong barrier against fruition. When that time comes (but better even before the time comes), we will need to have a serious conversation about machine policy and ethics (ironically both fundamentally human subjects), but for now, we'll allow AI to steadily improve and run amok in society.
Rockwell Anyoha is a graduate student in the department of molecular biology with a background in physics and genetics. His current project employs the use of machine learning to model animal behavior. In his free time, Rockwell enjoys playing soccer and debating mundane topics.
This article is part of a Special Edition on Artificial Intelligence.

For more information:

Brief Timeline of AI
https://www.livescience.com/47544-history-of-a-i-artificial-intelligence-infographic.html
Complete Historical Overview
http://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf
Dartmouth Summer Research Project on Artificial Intelligence
https://www.aaai.org/ojs/index.php/aimagazine/article/view/1904/1802
Future of AI
https://www.technologyreview.com/s/602830/the-future-of-artificial-intelligence-and-cybernetics/
Discussion on Future Ethical Challenges Facing AI
http://www.bbc.com/future/story/20170307-the-ethical-challenge-facing-artificial-intelligence
Detailed Review of Ethics of AI
https://intelligence.org/files/EthicsofAI.pdf

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The History of Artificial Intelligence - Science in the News

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by Rockwell Anyoha Can Machines Think? In the first half of the 20th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the "heartless" Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis.

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Amazon, Ebay, Zalora, Alibaba, Lazada....

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Pieter de Villiers, Clickatell Co-Founder and CEO.

getty

Covid-19 has brought about all kinds of changes in society, including how companies interact and transact with their customers, which is now most often at home and likely via a mobile phone. But while the pandemic has hastened the evolution of digital commerce, the seeds for the next iteration - chat commerce - were actually planted several years ago.

If you're unfamiliar with the term, think of chat commerce as the simple act of using your favorite chat app on your mobile phone to make a purchase, get a service or request support. You simply send a message to query a bill, explore a product or service or make an acquisition.
The Acceleration Of Chat Commerce
Chat commerce history goes back a little over 20 years and is founded at the convergence of mobile phones and the internet. That convergence is now seamless, so it's easy to forget that less than 25 years ago, phones and the internet were two very different worlds. Phones were for talking and texting, and the internet was for browsing and commerce. Phones and computers weren't connected and didn't interoperate.

And then everything changed.

Chat commerce, a form of digital commerce that's still emerging in the U.S., enables brands to interact and transact with customers through messaging from anywhere as well as securely and in real-time. Brands can "go where their customers are" and serve them on chat apps rather than needing to put customers on hold during a call, requiring customers to download another app or asking customers to access the website to re-enter payment credentials.

Many consumers are choosing chat as their preferred channel, and that means brands will follow. The data indicates that consumers spend about 90% of their screen time on five mobile apps, according to a 2016 Forrester report, and within that, chat apps rank number one. People chat with friends and family so pervasively that it's not unusual for people to use chat apps within the confines of their own home to say to someone, for example, "Have you started your homework yet?" or "Dinner's ready."
Part Of The Consumer Experience
Using chat to interact with businesses is already experiencing widespread adoption and interest. As the co-founder and CEO of a company that provides chat commerce, I've found that some early adopters are also realizing the benefit of transacting within chat. A few years back, many brands launched chatbots in their customer service operations, but they learned that unleashing an automated chatbot on a customer does not equate to "customer service." In fact, you might call it a customer dis-service.

Chat commerce is much more than a chatbot or live chat experience. It entails the full commerce journey from product discovery, placing an order, making payments, tracking deliveries, chatting to a live agent, resolving issues and providing feedback. Therefore, the technology stack needs to support the entire customer experience lifecycle to unlock the full potential of commerce in chat. Chat commerce is also not only about AI, business process automation or payments; it is also about the ability to place a brand inside the address book of its customers, right next to their family and friends, and serve them in a way commerce was always intended - simply, naturally and anytime.
Best Practices For Commerce In Chat
Building meaningful customer relationships that last relies on providing successful engagements - every time. These should be convenient and result in trust-building moments of truth where your customers find what they expect and can get things done. From checking the status of an account, ordering a meal, tracking a package in the mail, asking about inventory or paying a bill, this is all chat commerce. What's more, once the customer has the information they need and if the customer journey and engagement allowed them to build trust, I've observed that they often convert to also making the transaction in the same channel. In short, the same experience we expect in a store can be orchestrated over their chat channel of choice.

While payments in chat often complete the chat commerce experience, it's also true that most of the initial engagements over chat might not result in a transaction. Therefore, you should start the digital transformation journey on chat by focusing on providing excellent self-help solutions and live assisted chat customer support. It won't be long before customers add the business contact to their address book and confidently reach out whenever they need assistance. These trusted brand relationships can result in valuable repeat business.

To convince customers to transact with you in chat, be sure to support a wide range of payment methods and allow your customers to make payments instantly and in a way they are already familiar with, whether that's a credit/debit card, instant EFT or wallet payments. Another big factor is to safeguard against fraud.
Here's To The Future
For each use case, determine the level to which your customer must be authenticated to provide the appropriate level of access. For example, with FAQs, there is no need for authentication, but when your customer asks for an insurance policy, I recommend two-factor authentication. This allows you to verify that your customers are in possession of their mobile device associated with their phone number by sending an OTP to the device.

Following these simple guidelines can help you cost-effectively and securely use chat for transactions.

The rewards after getting customer engagement in chat right can be massive. In partnership with our company, Aberdeen Research recently published a report, "Maximize the ROI of Chat in Commerce Activities," which found that companies that include chat experience 75% year-over-year growth in annual revenue and 89% annual growth in cross-selling and up-selling revenue.

Now that the pandemic has forced brands to interact and transact with customers where they are, there has never been a better time to embrace commerce in chat and get closer to your customers. As recent history has shown, what once seemed novel can soon seem as if it's always been here.

Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?

... Read More

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Council Post: How Chat Commerce Is Changing E-Commerce

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Pieter de Villiers, Clickatell Co-Founder and CEO. Covid-19 has brought about all kinds of changes in society, including how companies interact and transact with their customers, which is now most often at home and likely via a mobile phone.

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The idea rose to mainstream prominence in 1999 with the launch of Napster, a P2P software product that allowed people to trade MP3 music files with one another.

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Scientific research and data which is created by the collaboration of people all over the world via technology.

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noncommercial software shared freely and developed collectively on the internet

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Technique for making a computer produce better results by learning from past experiences and data.

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A system that determines the precise position of something on Earth through a series of satellites, tracking stations, and receivers.

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Code.org activity
https://trends.google.com/trends/explore

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Assistive technology for learning

Assistive devices

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https://www.smartgrid.gov/the_smart_grid/smart_grid.html

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How might video conferencing effect business?

https://www.pewresearch.org/fact-tank/2020/03/31/americans-turn-to-technology-during-covid-19-outbreak-say-an-outage-would-be-a-problem/

https://www.wired.co.uk/article/video-conferencing-era

Carbon Disclosure Project (CDP) researching video conferencing benefits for environment.

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What is Crowdsourcing?

Kickstarter is an example of crowdsourcing. It is a funding platform for creative projects. Every project creator sets their project's funding goal and deadline. If people like the project, they can pledge money to make it happen, share the link with others, ask questions. If the project succeeds in reaching its funding goal, all backers' credit cards are charged when time expires.

Why is crowdsourcing important?

Why is crowdsourcing important for creativity?

Khan Academy communication, collaboration and crowdsourcing

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Sensor networks are small nodes which gather data and stored on a computer.
https://www.postscapes.com/iot-healthcare/

Air Pollution Monitoring using Wireless Sensor Network | Keysight IoT Innovation Challenge Entry

Airvisual app

Wireless sensor networks for traffic monitoring in a logistic centre

Wireless Sensor Networks dedicated to Structural Health Monitoring (SHM)

Khan Academy Smart sensors and networks

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https://www.google.com/maps/
Economic benefits of GPS

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Weather forecasting

Ebola outbreak model

Puzzle game about folding proteins

Social distancing simulation of sneeze/cough

Coronavirus simulations

Mapping simulations for eyecare

Khan Academy info on Simulations