High Quality Data Key to Eliminating Bias in AI

Biases are an incurable symptom of the human decision-making process. We make assumptions, judgements and decisions on imperfect information as our brains are wired to take the path of least resistance and draw quick conclusions which affect us socially as well as financially. The inherent human “negative bias” is a byproduct of our evolution. For […]

Biases are an incurable symptom of the human decision-making process. We make assumptions, judgements and decisions on imperfect information as our brains are wired to take the path of least resistance and draw quick conclusions which affect us socially as well as financially.

The inherent human “negative bias” is a byproduct of our evolution. For our survival it was of primal importance to be able to quickly assess the danger posed by a situation, an animal or another human. However our discerning inclinations have evolved into more pernicious biases over the years as cultures become enmeshed and our discrimination is exacerbated by religion, caste, social status and skin color.

Human bias and machine learning

In traditional computer programming people hand code a solution to a problem. With machine learning (a subset of AI) computers learn to find the solution by finding patterns in the data they are fed, ultimately, by humans. As it is impossible to separate ourselves from our own human biases and that naturally feeds into the technology we create.

Examples of AI gone awry proliferate technology products. In an unfortunate example, Google had to apologise for tagging a photo of black people as gorillas in its Photos app, which is supposed to auto-categorise photos by image recognition of its subjects (cars, planes, etc). This was caused by the heuristic know as “selection bias”. Nikon had a similar incident with its cameras when pointed at Asian subjects, when focused on their face it prompted the question “is someone blinking?”

Potential biases in machine learning:
  • Interaction bias: If we are teaching a computer to learn to recognize what an object looks like, say a shoe, what we teach it to recognize is skewed by our interpretation of a shoe (mans/womans or sports/casual) and the algorithm will only learn and build upon that basis.

  • Latent bias: If you’re training your programme to recognize a doctor and your data sample is of previous famous physicists, the programme will be highly skewed towards males.

  • Similarity bias: Just what it sounds like. When choosing a team, for example, we would favor those most similar to us than as opposed to those we view as “different”.

  • Selection bias: The data used to train the algorithm over represents one population, making it operate better for them at the expense of others.

Algorithms and artificial intelligence (AI) are intended to minimize human emotion and involvement in data processing that can be skewed by human error and many would think this sanitizes the data completely. However, any human bias or error collecting the data going into the algorithm will actually be exaggerated in the AI output.

Gender bias in Fintech

Every industry has its own gender and race skews and the technology industry, like the financial industry, is dominated by white males. Silicon Valley has earned the reputation as a Brotopia due to its boy club culture.

Blockchain, Machine Learning Projects Take the Lead at Dun & Bradstreet

You wouldn’t normally think of a 177-year-old company as one that would be on top of digital transformation and emerging data analytics trends. But Dun & Bradstreet has always been a data business, way back in 1841 as the Mercantile Agency, a credit information bureau. Today the new technologies are a little different than they were in the […]

You wouldn’t normally think of a 177-year-old company as one that would be on top of digital transformation and emerging data analytics trends. But Dun & Bradstreet has always been a data business, way back in 1841 as the Mercantile Agency, a credit information bureau.

Today the new technologies are a little different than they were in the 19thcentury. But the mission is the same — to provide data and analytics services for business.

As Dun & Bradstreet’s Global Leader of Data Innovation, Saleem Khan is helping navigate the company’s efforts as it creates new data and analytics services to offer its customers. A couple of the technologies at the top of his list right now are Blockchain and machine learning. He told InformationWeek in an interview about some the trends in the market right now around these technologies and what plans Dun & Bradstreet has in mind for using these technologies at the company.

Identity Management Using Blockchain

One of the things that Khan is looking at is identity management using Blockchain. When one entity sends a bitcoin to another, Blockchain ensures trust in the transaction. But how do different entities in an increasingly complex world, trust other entities they may not know? How do you know someone is who they say they are? And, our knowledge of what you are like may change over time.

“Identity, from our perspective, is dynamic,” Khan said. “It changes over time. You may be a good actor today, but you may not be a good actor a year from now. You may be bankrupt.”

Among the projects on Khan’s agenda is creating a strategy around managing those identities for companies. There are many companies offering business-to-consumer services in this manner, he said, but there aren’t so many doing business-to-business.

Dun & Bradstreet is launching a pilot of 6,500 publicly traded US companies to load more of that type of information into a Blockchain as a node with other trusted networks, Khan said, to provide real-time identity data.

Khan is also exploring a project to provide master data management-type services for the Blockchain ecosystem. He said that there are multiple vendors out there for Blockchain right now — Hyperledger, Ethereum, R3 — much as there were several vendors for databases in the early 1990s — Oracle, DB2, and Sybase to name a few. Master data management efforts helped organizations working with multiple data systems to create a single point of reference, and Dun & Bradstreet is working on a way to do that with all the different Blockchain systems, according to Khan, and the company has patents pending in this area.

Machine Learning Projects

Khan has also spearheaded couple of machine learning projects. The first had the goal of determining the SIC codes (standard industry classification) for all the companies in the UK. Knowing the classification of different companies can be important to, say, insurance companies. For instance, they would want to know whether you were a toy retailer or an explosives manufacturer before they assessed your risk profile.

Khan’s group took the whole UK database of companies and found all the company websites, scraping words from the sites. Dun & Bradstreet then ran all that language through a neural network that had been trained to determine company type by words used. Sure, it’s a job that humans could have done. But point this neural network at the work, and it gets done a lot faster. Khan estimates the project, done by humans, would have taken 2.5 years and $10 million to go through the entire database in the UK and update all the SIC codes.

“We can now do this in a matter of hours,” he said.

Dun & Bradstreet’s staffing for the project was a hybrid effort, using talent from both inside and outside of the company.

“Machine learning is a space where it’s tough to get skilled resources,” he explained.

Other machine learning projects are also based on text and natural language. Khan said that his group has been working on predicting companies that will declare bankruptcy by looking at the words in filings and news reports that typically precede bankruptcy in an effort to predict it.

“We are finding those concepts and triggers within the business that lead up to bankruptcy,” he said.

And yet such an approach is not without risk. In an era where “fake news” and bad actors may try to steer your results one way or another by creating news reports that are not genuine, machine learning can sometimes be deceived. That’s why Dun & Bradstreet is keeping the human element in place, too.

“We want to make this more of a human/machine hybrid approach,” Khan said. “We look at algorithms and machine learning as prep tools. We do need a human to do that final check.”

Read the source article in InformationWeek.com.

Supply Chain is ‘Killer’ Blockchain Use Case, says IDC

After more than enough hype, blockchain use cases are lately becoming more numerous and real. In fact, improving supply chain processes will be the “killer” among blockchain uses cases, according to Bill Fearnley, IDC research director of worldwide blockchain strategies, who spoke at the IDC Directions 2018 conference held recently in Boston. “I get asked often […]

After more than enough hype, blockchain use cases are lately becoming more numerous and real.

In fact, improving supply chain processes will be the “killer” among blockchain uses cases, according to Bill Fearnley, IDC research director of worldwide blockchain strategies, who spoke at the IDC Directions 2018 conference held recently in Boston.

“I get asked often ‘What are the killer [blockchain] use cases?’ and [supply chain] is it,” Fearnley said. Blockchain ledgers can help improve the supply chain in three ways: shipment track and trace, inventory management and proving a product is genuine.

“Increasingly consumers and manufacturers are getting more concerned and more aware of country of origin,” Fearnley said. “For example, you’re trying to make sure that you’re not getting conflict minerals — you want to make sure that the country of origin is the right place to be buying products from, that it’s not in a war zone or from a criminal element.”

Other blockchain use cases are found in financial services, manufacturing and distribution and government services, Fearnley said.

A Foundation for Digital Trust

“Blockchain is the new foundation for digital trust which drives digital transformation and business transformation at scale,” he said. “This changes security and trust both between companies — suppliers, distributors, retailers–– and also between you and end-user customers and consumers.”

Improved data security is the primary reason blockchain can reach killer application status, particularly for supply chain and financial services, Fearnley explained. Records are created in a ledger that’s secured with advanced cryptography, and all the records are linked, so they are immutably chained in theory. The records are sequential and the ledger gets longer as more records are added, making it more secure the longer it gets.

“The immutability of the blockchain record makes this a very attractive solution for some of the most data secure organizations in the world, including supply chains, financial services, [healthcare] and governments,” Fearnley said.

This security allows companies to have trusted records that they can use internally and extend externally. For example, an international company could share information among its operating divisions across borders, or manufacturing could share information with sales and marketing. They can then extend that ledger to partners or regulators.

The growth of blockchain has occurred rapidly in the last few years, Fearnley said.

“In 2016, most of the conversation was ‘What is this, what is blockchain, what is distributed ledger, smart contracts?'” he said. “The tone and timbre of the conversation changed in 2017 to ‘Why should we care?’ and by 2018 we’re hearing more questions about ‘How are we going to do this?'”

This growth in blockchain use cases will continue: IDC estimates that worldwide blockchain spending will amount to $10 billion by 2021. The spending will be distributed fairly evenly throughout industries, as IDC estimates that 35% will occur in financial services, 25% in distribution and services, 20% in manufacturing and resources and 10% in public sector.

Read the source article at TechTarget.com. 

SingularityNET vs Effect.ai

SingularityNETTLDR; SingularityNET is winning the battle to create a blockchain-powered AI-As-a-Service platform.Mike Yap recently asked me:What are your thoughts on these two AI blockchain companies: https://singularitynet.io/ + http://effect.ai/ ?I w…

SingularityNET

TLDR; SingularityNET is winning the battle to create a blockchain-powered AI-As-a-Service platform.

Mike Yap recently asked me:

What are your thoughts on these two AI blockchain companies: https://singularitynet.io/ + http://effect.ai/ ?

I will gladly offer my opinion on the two companies — SingularityNET and Effect.AI.

Vision

SingularityNET and Effect.ai share a similar vision. SingularityNET is a open-source “protocol for AI algorithms and models” which aims to provide “access a global library of AI tools” by “combining open source principles, blockchain integration” and machine learning. (SingularityNET, 2018) SingularityNET is “collection of smart contracts for a decentralized market of coordinated AI services.” This allows anyone to “add an AI/machine learning service to SingularityNET for use by the network” (SingularityNET Whitepaper, 2017). On the other hand, Effect.ai is a “blockchain-powered, decentralized platform for Artificial Intelligence development and AI related services.” Effect.ai will build an “open marketplace for offering AI algorithms as a service.” Also, “within this marketplace, algorithms will have the ability to communicate and collaborate with other algorithms and purchase services from each other.” (Medium, 2018) In other words, SingularityNET and Effect.ai allow developers to offer their artificial intelligence algorithms on the platform. Users of SingularityNET and Effect.ai can purchase access to AI services based on smart contracts.

Funding

SingularityNET is ahead of Effect.ai in terms of funding. “SingularityNET token sale completing in 66 seconds, raising $36 million (in ETH)” on December 24, 2017. (SingularityNET, 2018) That’s 25 billion in USD (as of December 24, 2017) (CoinMarketCap, 2017) On the other hand, Effect.ai has currently raised $13 million in USD (Effect.ai, 2018)

Development

SingularityNET is ahead of Effect.ai in terms of development. SingularityNET project is publically available via open source, first released 7 months ago (Github, 2017). The SingularityNET Alpha Network is already live (Medium, 2018). Effect.ai has not yet disclosed the date of the public release of code.

Partnerships

SingularityNET is ahead of Effect.ai in terms of partnerships. SingularityNET already has established partnerships with: BitSpace, Ocean Protocol, Nexus, FundRequest, Hacken, and NR Capital (Medium, 2018). Effect.ai partnerships have not been disclosed. (Effect.ai, 2018)


SingularityNET vs Effect.ai was originally published in Produvia on Medium, where people are continuing the conversation by highlighting and responding to this story.

Executive Interview: Driving AI Innovation: New Services, Investment and Societal Good are Goals of XPRIZE Competition

“The question that everyone needs to ask themselves is, while we are building new innovative approaches with AI, how could we also think about bringing value to society?” – Amir Banifatemi Amir Banifatemi is the Prize Lead of the IBM Watson AI XPRIZE. Amir has more than 25 years of experience in development and growth […]

“The question that everyone needs to ask themselves is, while we are building new innovative approaches with AI, how could we also think about bringing value to society?” – Amir Banifatemi

Amir Banifatemi is the Prize Lead of the IBM Watson AI XPRIZE. Amir has more than 25 years of experience in development and growth of emerging and transformative technologies. XPRIZE is a global leader in designing and implementing innovative competition models that aim to solve the world’s greatest challenges and to encourage technological development to benefit humanity.

Q. How would you describe the big picture mission of XPRIZE?

XPRIZE, basically, is an innovation engine. We find opportunities for radical transformation of society through incentivized participation of the crowd. So we leverage exponential technologies, which are the most advanced technologies, deep technologies such as artificial intelligence, blockchain, quantum computing, genetic engineering, IoT sensors, 3D printing and so on. We seek to enable breakthroughs and exponential progress of those. We are preparing society for the future by trying to identify leveraged ways to bring radical innovation to everyone.

Q. Is that what the leverage is, the prize?

Many factors come into play when you talk to teams about motivation for being a part of the XPRIZE competition. Teams say that the ecosystem, resources available and competitive spirit are front of mind – all of these drive investment into the challenge area. In many cases, the prize does factor into the equation. In certain areas, for instance, government spending might not be enough or there is no incentive for governments to spend resources, or when venture capital is not investing into certain areas, or research has not been progressing. One way to look at an XPRIZE is an opportunity to accelerate and find teams to work on those topics that have not made progress or are not going to make progress in a reasonable amount of time.

The same thing that happened about almost 100 years ago, when the first commercial aviation began. A prize of $25,000 incentivized people to travel by air from New York to Paris. Charles Lindbergh won that prize and that opened up the whole aviation industry. The number of people trying to be creative and innovative about aviation — engines, everything — went through the roof so that opened up the whole aviation industry at that time.

And the same way, today, we can say that in 2003 with SpaceShipOne winning the $10 million prize to incentivize engineers to create a craft that would transport a three-person crew 100 kilometers into the atmosphere. That was really the beginning of commercial space travel. Virgin Galactic purchased the license of that and started Virgin Galactic. A number of startups and research then spun out and the whole space industry opened up. And, today, you see how many people are trying to go to space, to go to Mars, to explore other planets. That was basically the opening.

So what we’re saying about leverage is that when we open up an opportunity to a number of teams with a very strong innovation challenge and people respond positively to that challenge, then a number of innovations happen and unlikely teams get created. Because many, many people may have opportunities and ideas about solving a problem who may not come forward, necessarily. So we create an incentive.

Q. XPRIZE in December announced 59 teams advancing in the $5 million dollar competition for the Watson IBM Watson AI XPRIZE, which is a four-year global competition. Where are you now on that schedule?

The global competition started in June of 2016. We invited teams to come and compete for that prize, sponsored by IBM. Meaning, that the purse is given by IBM for the winners, therefore the name of IBM Watson on the prize. And at that time, about 10,000 requests came through; 800 teams got started. Out of those 800 teams, 150 teams got an approval by the judges to start the competition because they demonstrated the needed capabilities. The competition is a four-year competition with different milestones.

The first milestone is year one, where we go from round one to round two. And to go to round two, teams have to demonstrate that they’re working on meaningful projects and they are tackling the guidelines of the competition. So 59 teams out of the 150 demonstrated that, so these 59 that we announced in December. Now we are going into the second year, engaging in solution development. At the end of 2018, the teams will submit their work for judges to review and decide who is going to round three. Round four will be the semi-final; the finalists will be on the TED stage in 2020. So this stepping stone to a prize is usually the way an XPRIZE functions. It’s always a longer process. It’s not a three- or six-month effort. And, today, teams are gradually engaging into their solution development, now.

Q. In your release, you announced the top 10 teams. Could you comment on what you’re seeing from these AI innovators?

Yes. It doesn’t mean that these teams are going to win. It means that at this phase, on round one, these are the teams that demonstrated more advancement and have put more work into the competition. The domains in which they are working include health and wellness, civil society, space and frontiers, learning and human potential, shelter, energy, and planet and environment.

So these teams are actually working on very specific problems related, for instance, to managing depression, or helping with democracy, or helping with improving the environment and clean water. Some of the 59 teams are tackling problems in the same domain, but they definitely have different focuses and the type of AI and technology they’re using is very varied. We may not have full knowledge of what they’re doing yet. We have validated that they’re working on something meaningful, which has impact attached to it and they have demonstrated that they know what they’re talking about and they have the technical abilities to deliver. What they’re actually going to be delivering, we will see in the next coming months because they’re actually building it.

Q. How do you characterize the entrants? Are they practitioners in the field who are working in business? Or are they students or both? Or is all types?

We have actually a good mix of teams created with individuals coming from academia, from research, from corporations; most of them are startups. There are a few students but not many. Teams have to be multidisciplinary because to tackle AI for impact, you have to tackle both — AI and impact. So if you’re talking about a project such as improving enterprise quality control, for instance, you need to know something about quality control and you need to know something about AI. So for that reason, teams are usually multidisciplinary, have both sides, the AI side and the AI/machine learning and everything that’s related to AI but also the domain, the expertise of the domain. So those teams are not just teams of students, or just a team of academics, or just a team of corporate people, or just a team of business people. These are very much mixed teams.

Q. What is the best use of AI for the most impact?

I’m trying to bring attention to the fact that AI can used for the most immediate and pressing issues that society faces. Whether that issue is unemployment or economic output or healthcare or education. And corporations have to be imaginative and modern in creating new products and more wealth and more jobs. Every now and then, some technologies come forward and give us the opportunity to think better about applications built with them. Machine learning and artificial intelligence give us more power to predict, to analyze, to understand, and to create more automation and to learn more with data. So we ask how can we employ these capabilities to problems that are definitely more important to tackle, such as for everyone to have a better life, to have better welfare, to participate more in democracy and to basically have better skills and to be employable and so forth. This is how we look at impact.

The question that everyone needs to ask themselves is, while we are building new innovative approaches with AI, how could we also think about bringing value to society? So think of it as a corporation that makes money and profits and generates new value, and is also thinking about being sustainable, having less impact on the environment and helping better its employees and its community. So AI gives us the opportunity to ask a number of questions. Of course, artificial intelligence is built with algorithms, computing power, data. Most of the time it can be autonomous. Sometimes it cannot be autonomous, and it has to be used with human help. The amount of information that we inject into AI programs certainly contains our biases, and certainly exposes people to less privacy. How can we also be aware of those? So that question about impact brings a lot of other questions next to it, which I’m suggesting we should be aware of and think about. Corporations have a role, definitely, to participate in this dialogue. And while they’re pursuing reinvention of their businesses with AI, I think there’s an opportunity, also, for them to understand the implications of AI through the whole company itself and the ecosystem in which they operate.

Q. What is the impact of AI on corporations reinventing themselves?

Today AI is set to transform all business in ways that we have not seen since the industrial revolution. So, really, we are in a new revolution. And, fundamentally, AI is helping business reinvent how they run, how they operate, how they compete, how they thrive, how they create value. So if, for instance, technologies help  to lower costs and create new jobs or create new growth opportunities — this is how AI is basically helping enterprises reinvent themselves.

Maybe the type of jobs and skills they need to incorporate is going to be different. Maybe the training of employees has to happen sooner, maybe lowering costs and increasing productivity helps us generate better profits that could be repurposed to other areas. Maybe some aspect of jobs will be lost and then can be converted into something else. This can help not only enterprises, but it also helps governments, nonprofits, and the society as a whole. And I think the understanding of that topic is critical. So the impact of AI on corporations is really the opportunity to think again about, one, how can we create value? And second, how can the tools of productivity and growth play out, again, with AI as an ingredient?

Many people say that AI will boost profit and innovation. Some people say that AI will lead responsiveness. Some people think that AI is going to bring more inclusion. Some people think that AI will increase automation so that jobs will be lost. All of these are interesting to consider. The impact is going to be central to each organization to decide upon and take action. And I think that opportunity, again, the same way it was important for society, it’s important for enterprises because it’s part of the core of creating jobs and value and wealth. And because products and service are fundamental to the growth of enterprises, AI is definitely a conversation to have. I think all big knowledge partners, consultancies — everyone basically says the same thing about AI helping enterprises reinvent themselves today.

Q. What would you say to people who fear AI?

For many people, AI is unknown. Many uses of AI are flying somewhat under the radar for most people. We’ve seen AI used for dramatic effect, and for some, the knowledge of AI comes, mostly, from science fiction and media portraying AI always in the form of a Terminator or dystopian face or dystopian scenario. AI in the real world is different. AI is, as a core, is a set of technologies that help us automate certain things. Automation will probably make our life better. Automation will help us do things stronger, better, faster, in a larger scale.

And any new tool probably creates a number of fears and the fear of unemployment is probably the number one fear today. However AI is allowing us to create new jobs as well. The fear of losing privacy and being observed is a second fear. I personally don’t subscribe to those fears as long as, we, as a society, as a group, as a collective, take actions to make sure that AI is built in a beneficial way.

AI creates new ways for us to reboot everything and think about it and talk about it. But I don’t transfer that into fear. I transfer that into a responsibility that we have to ask those hard questions and make sure that we have good boundaries around that and good checks and balances and make sure that AI is, first, beneficial and built safely. And, second, that we use AI for the right reasons and we don’t put it everywhere and that we have some responsibility and some third-party check-in’s and so forth.

Q. Are there key areas of AI where you’re seeing innovation today?

AI is advancing very fast in certain areas, such as healthcare, which has seen tremendous benefits. Imagine cognition disease diagnostics, assisting doctors with robotics, assisting with some medical therapeutic acts and, basically, planning in general of healthcare issues. But, also, in education, where personalized learning is becoming more important. You can have more interactions, you can incorporate automation, in terms of certain types of experiences in learning. And AI is helping with climate and weather and water management, so climate and environment is getting benefits from it. Of course, everyone sees self-driving cars as automation happening on the road, where we can now detect objects and manage the car movement at a certain level. Today, we’re not at full capacity of level five autonomous driving but we are close to level three. And teams and labs and corporations are working on improving that. These are the low-hanging areas where we see immediate application but corporations, government, everyone is looking at AI in everything — from economic participation, to democracy, to helping psychology issues or helping committees connect better — we’ve seen so many examples just with this competition. But this competition represents only a tiny, a very tiny fraction of all the ideas and possibilities that many people are working on today.

Q. What message should readers of AI Trends take away?

Well first, and foremost, I think it’s important that everyone one of us understands better what AI is and is not. And understand what it can do and try to participate in dialogue and conversations around privacy, ethics and governance of AI. Test products, give feedback, and participate with groups and teams that are working on hard topics and are trying to collaborate and collectively make sure that AI is well-understood. It’s a real change and I think participating, understanding, and discussing it and sharing the right facts and data is important. Today we have opportunities to showcase practical applications that benefit everyone. Let’s identify those and push those first to be implemented, for the majority of people to benefit and to create a better society. So I will say this is the first stage that we can focus on and we have a responsibility to focus on it.

For more information, go to XPRIZE.org.

How Blockchain Technology and Cognitive Computing Work Together

When it comes to revolutionary technology, the blockchain and cognitive computing are two at the top of the list in 2018. With these technologies finally being put to use in practical applications, we’re learning more and more about what they can do on their own—and together. Let’s take a look at how some industries can […]

When it comes to revolutionary technology, the blockchain and cognitive computing are two at the top of the list in 2018. With these technologies finally being put to use in practical applications, we’re learning more and more about what they can do on their own—and together. Let’s take a look at how some industries can take advantage of this powerful combination.

Before we can discuss what these two technologies can accomplish together, it’s important to understand them separately.

Cognitive computing is essentially using advanced artificial intelligence systems to create a “thinking” computer. Deep learning allows cognitive computers to learn and adapt as they receive new data, and they do not simply execute logic-based commands as computers have traditionally done. Because the technology is evolving and encompasses many different AI systems, there is no standardized definition for these systems. However, the term is best used to describe computer systems that mimic the human brain.

The blockchain, a new system for storing information and processing transactions, was created for the distribution of bitcoin, the world’s leading cryptocurrency. It’s different from most databases because it uses a distributed ledger system, rather than a centralized database. In basic terms, that means that the information is distributed in thousands of computer networks, instead of being stored in one location. The information is updated regularly, and everyone on the network can view it.

This makes the blockchain more secure than a traditional database since a hacker cannot compromise the whole system by breaching one computer. Today, the blockchain is becoming popular in some industries for its superior security. Cybersecurity is a growing concern, and the blockchain could be one way some industries can reduce the number of breaches.

Cognitive Computing and Blockchain – the “IoT Dream”

So how do these two technologies work together? Since we’ve only just scratched the surface on the capabilities of both the blockchain and cognitive computing, there’s still a lot of opportunity for bringing these technologies together. One of the largest areas for potential expansion is in tandem with IoT (Internet of Things) growth.

Many industries are beginning to see how using interconnected devices can help them automate and improve their processes, but there are currently limitations on scaling and security with centralized systems.

IBM, a leader in artificial intelligence, has already integrated its Watson supercomputer into a platform for IoT, allowing businesses to make better use of the data they collect using these devices. IoT devices collect the data, but the majority of this data is “dark”, meaning that it just sits in storage and isn’t used for anything. Cognitive computing has the ability to process this data in ways humans can’t—while gaining valuable insights that can be used in strategic planning and performance measurement.

So how does the blockchain fit into this equation? Mainly, as a way to scale IoT usage and for security purposes. IoT data can be extremely sensitive and valuable to businesses—the last thing a company wants is for a data breach to occur. Blockchain ledgers also create logs for context, which provide detailed information about anomalies and problems and break down exactly where and when these problems occurred.

  • By Sarah Daren, consultant

Read the source article at RTInsights.com.

Blockchain – The Strong Backbone for Businesses

What is Blockchain Technology? How one blockchain can have Infinite possibilities & opportunities in hand in this red ocean market world. Total Reading Time – 7-8 Minutes Some background Blockchain as on date is mystery story for many (Including my self). We have heard lot that it yields […]

The post Blockchain – The Strong Backbone for Businesses appeared first on Vinod Sharma’s Blog.

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What is Blockchain Technology? How one blockchain can have Infinite possibilities & opportunities in hand in this red ocean market world. Total Reading Time – 7-8 Minutes Some background Blockchain as on date is mystery story for many (Including my self). We have heard lot that it yields […]

The post Blockchain – The Strong Backbone for Businesses appeared first on Vinod Sharma's Blog.

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Blockchain and Swarm Intelligence

Swarm Intelligence is a new form of A.I. that utilises technology that can leverage natural human instincts – intuitions, emotions and sensibilities, while making a decision by combining the best of both worlds. 

The post Blockchain and Swarm Intelligence appeared first on Vinod Sharma’s Blog.

Swarm Intelligence is a new form of A.I. that utilises technology that can leverage natural human instincts – intuitions, emotions and sensibilities, while making a decision by combining the best of both worlds. 

The post Blockchain and Swarm Intelligence appeared first on Vinod Sharma's Blog.

What Can You Expect From Chatbots In 2018?

A box pops up in the bottom-right corner of your screen. Text appears. “Hello. How can I help you today?” Is it a person or a machine? It’s becoming harder to tell. Welcome to the world of chatbots. Chatbots: Big Nuisance Or The Next Big Thing? Sometimes seen as a nuisance, it’s easy to dismiss […]

A box pops up in the bottom-right corner of your screen. Text appears. “Hello. How can I help you today?” Is it a person or a machine? It’s becoming harder to tell.

Welcome to the world of chatbots.

Chatbots: Big Nuisance Or The Next Big Thing?

Sometimes seen as a nuisance, it’s easy to dismiss this ubiquitous technology as a functionless frivolity.

That, however, would be a mistake.

While previous iterations of chatbot technology may have felt a little like conversing with a lifeless automaton or browsing a fancy FAQs, modern incarnations are anything but.

Today’s chatbots utilize advances in natural language processing (NLP)—the ability to interpret the nuances of everyday language, and give a relevant response—to have smart, helpful and personalized conversations with customers. They can provide service around the clock, have huge catalogues of knowledge and never get tired. That’s why 80 percent of businesses want to use chatbots by 2020. And it’s why 44 percent of US consumers already want to deal with chatbots rather than humans when it comes to customer relations. 

It’s clear that chatbots are a technology worth investigating, if not investing in, for any business. But what about the future? What will 2018 bring for the technology? Let’s take a look at the trends you need to know about.

Voice Speaks Volumes  

Unless you’ve been living under a rock for the past year, you’ll have heard of the Amazon Echo and Google Home. These voice-based devices (using the developments in NLP mentioned earlier) have transformed how people interact with the technology around them.

Watch out for companies like Storyline and PullString to have an impact in this arena.  

AI Makes Chatbots Smart(er)  

Linked to the rise of voice-based chatbots will be the further development of artificial intelligence (AI).  

AI has experienced a quantum-leap forward over the past few years (in fact, it’s moving faster than anyone expected — but business owners still struggle to gauge its full impact).  

In 2016, Google’s AI, AlphaGo, shocked the world by beating 17-time world Go champion Lee Sedol. Go—a game of perfect information (no luck-based elements)—was a tough nut to crack for AI. To win requires what many describe as ‘intuition’, so for AlphaGo to beat the world champion was a big deal.  

 How did it achieve this feat? It taught itself. It played multiple-lifetimes’ worth of games in just a few months, effectively training itself to be the best in the world.  

How does this relate to chatbots? Well, now that this technology exists, it’s just a case of extrapolating its potential uses. Picture an AI like AlphaGo as a chatbot, tasked with customer service. It could feasibly teach itself every and any issue that a customer might come up with, as well as the best possible solution. This could mean instant answers to any question. No matter how complex your query, a future AI chatbot wouldn’t miss a beat.  

Sounds good, right? (That is, forgetting the potential Terminator scenario.)  

Omnichannel Chat And The IoT  

2018 represents a real opportunity for chatbots to go omnichannel and embrace the Internet of Things (IoT).  

 Currently, you might interface with a chatbot on a single platform—a website or an app, for example. But what happens when the conversation you’re having needs to take you onto another platform, or even another device? That’s where chatbots could be heading in 2018.  

There are already fantastic examples of chatbots taking an omnichannel approach. KLM, for example, created a chatbot for use in Facebook Messenger. With it, KLM travellers can ask questions about flights, access their boarding passes and be put through to a human representative, if necessary. Their whole journey, so to speak, is catered for by the chatbot. This is the kind of experience that’s set to become the norm this year.  

The next stage, however, is even more exciting. It’s where we begin to see chatbots using the IoT to interact with other devices. Let’s say, for example, that your new smart TV stops working. You get on your phone and start talking with a chatbot for help. Thanks to the connectivity offered by the IoT, not only is that chatbot able to identify your issue, it can also connect with your TV and solve the problem there and then. Job done.  

Blockchain Brings Security To Conversations 

Blockchain is a term you’re probably familiar with thanks to its reputation for securing Bitcoin. But blockchain encryption will, in 2018, have an impact on chatbots, too. 

In a nutshell, blockchain encryption is one of the strongest ways of stopping your information getting into the wrong hands (check out TechRepublic’s video for an in-depth explanation of how it works). And it will soon solve one of the major obstacles currently holding chatbots back from reaching their full potential.   

That obstacle is people’s willingness (or, rather, lack thereof) to divulge sensitive information.   

Though a multitude of ecommerce sites already offer free chatbots, they tend to be focused on small-scale customer service, marketing, or content engagement activities. 

If you were on your bank’s website and a chatbot asked you for your security details, would you hand them over? Or what if you were on a dating website and a chatbot asked you for personal information, would you tell it? The answer to both is probably no, and you’d be right to think that way.  

But what if you knew that any information you divulged was completely confidential and secured by the most thorough encryption known to man?   

Again, you might hesitate. But with time, and once you see the benefit it offers, it’s likely that you, and others, will start to feel more comfortable offering up private information to chatbots. That’s what blockchain brings, and that’s what we’re likely to see more of this year.

Wide-Scale Adoption, Even By Smaller Brands  

As chatbot technology becomes more widely available, more and more entrepreneurs and SMEs are going to be able to integrate them into their marketing and customer service strategies.   

In today’s hyper competitive ecommerce market, any new ecommerce investor would do well to go hard with chatbots and Messenger marketing in order to solidify their brand’s position as a market leader. It’s a surefire way to be competitive and drive sales and customer engagements. 

The technology is easy to implement and takes seconds to deploy — there is nothing to lose. 

What Next?  

This has really just been a taste of what new developments we’ll see for chatbots during 2018. Hopefully, it’s given you an idea of the massive potential this technology has for customer service now—and the even bigger potential we’ve yet to see.  

At the beginning of this article, we touched on the concept that it can be hard to tell if you’re talking to a chatbot or a person. You might think that it’s easy, and there’s no way a machine could fool you. At this moment in time, you might be right. But check again at the end of 2018, and let us know what you find. We expect you’re in for a surprise. 

  • By Victoria Greene, a branding consultant and freelance writer. See her blog at  VictoriaEcommerce. 

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