Will AI make us all multi-lingual?

It used to be Babel, but then Google Translate appeared and everyone started using it, regardless of the fact that what this tool often gave you was text that nobody could quite understand. And now it appears that tech companies are pouring money into developing Instant Translators. But why are they doing this?

AI language assistants are keen to help us all communicate in any language it seems and it does have an attraction for many people from diverse backgrounds. The race is on for translation in real time and Google, Apple, Amazon and Microsoft are all working on it and it is rumoured that Alexa will soon be able to provide real time translations and compete with Google Assistant. China also has a budding translator in Xiaomi’s Xiao AI.

All this is only possible because rapid advances in software can achieve speech recognition, speech synthesis, neural networks, and machine translation; all f which are necessary for real-time translation.

It makes sense that instant translators are only beginning to mature now, because of the technological advances. But what’s still not clear: why did companies start pouring money into them in the first place, and who will benefit from them?

The answer lies in the way the world is changing. Worldwide, there are now 250 million migrants — people who reside outside of their birth country — according to a recent Pew Research Study.  Plus, more than 60 million Americans speak a language other than English at home. By the middle of this century, all of these migrants stand to benefit from instant translation technology.  This applies to other countries in Europe and to Canada.

Refugees will benefit even more. Some 1.2 million people will be forcibly displaced from their homes in 2018 alone, according to the UN Refugee Agency and an inexpensive instant translation tool could help meet these people’s basic needs.

Ultimately, though, instant translators will help everyone, no matter their linguistic aptitude, and as we travel, language barriers won’t hold us back. So, perhaps there is much to be gained by the investment in AI instant translators.

 

 

 

Building trust in Artificial Intelligence

Trust is extremely important in human interactions, and it is also a vital element of the relationship between man and machine. Do you trust this software to do what it says? Is this brand of computers reliable than its competitor? Building trust in artificial intelligence (AI) also needs to be addressed.

Jesus Rodriguez, managing partner at Investor Labs, says, “Trust is a dynamic derived from the process of minimizing risk.” There are ways to approach this with software: testing, auditing and documenting all have a role in establishing the reputation of a software product. However, they are more difficult to implement with AI. Rodriguez neatly explains why: “In traditional software applications, their behavior is dictated by explicit rules expressed in the code; in the case of AI agents, their behavior is based on knowledge that evolves over time. The former approach is deterministic and predictable, the latter is non-deterministic and difficult to understand.”

So, what steps can we take to establish and measure trust in AI? At the moment confidence in an Ai product is highly subjective and often acquired without a clear understanding of the AI’s capabilities.

A team at IBM has proposed four pillars of trusted AI: fairness, robustness, explainability and lineage. What does each of them mean?

Fairness

“AI systems should use training data and models that are free of bias, to avoid unfair treatment of certain groups.” Establishing tests for identifying, curating and minimising bias in training datasets should be a key element to establish fairness in AI systems.

Robustness

“AI systems should be safe and secure, not vulnerable to tampering or compromising the data they are trained on.” AI safety is typically associated with the ability of an AI model to build knowledge that incorporates societal norms, policies, or regulations that correspond to well-established safe behaviours.

Explainability

“AI systems should provide decisions or suggestions that can be understood by their users and developers.” We have to know how AI arrives at specific decisions and be able to explain how it got there.

Lineage

“AI systems should include details of their development, deployment, and maintenance so they can be audited throughout their lifecycle.” The history and evolution of an AI model is an important part of building trust in it.

IBM also proposes providing a Supplier’s Declaration of Conformity that helps to provide information about the four key pillars of trusted AI. It’s a simple solution, and although it may not be the ultimate one, it represents progress in the world of AI.

A blockchain revolution in Accountancy

Blockchain technology is heralded as the game changer in so many fields: banking, currency, logistics just being a few of them, but there is one service area that nobody has talked about so much and that is accounting.

Most people would agree that accountancy isn’t quite as ‘sexy’ as banking, hence the lack of excitement about how the blockchain may completely revolutionise a service industry that is centuries old. As much as we like to make jokes about accountants, their services are invaluable to businesses and to entrepreneurs.

History shows us that double-entry bookkeeping, the foundation of all accounting, can be traced back to medieval Jewish merchants in the Middle East, and later picked up by Genoese merchants in the 14th century. From there it became the standard method and it is relatively simple — for every intake of money in one account (credit), there must be an equivalent outflow in some other account (debit).

Overall, accountants focus on managing risk. It allowed businesses to keep track of a number of transactions at the same time, and in a range of currencies. Accountants have a specialist skill set, but as John Katsos argues, the blockchain could potentially make many in the profession unemployed.

In traditional accounting there is room for errors and fraud, as many famous cases have shown. When a mistake happens, more accountants have to be brought in to correct it, and that leaves rooms for more errors. Katsos claims that the blockchain is not double-entry bookkeeping; it is “potentially infinite bookkeeping.”

As he says, “blockchain technology can give every user in a system an automatically updated list (a “chain”) of all transactions (“blocks”) that have occurred within that system.” Plus it has validators: designated members of the system who come to “consensus” over a transaction. The only limitation is the number of users and the amount of computing power available.

There is also the potential to use permissioned blockchain to avoid fraud. In this system, people using the blockchain have been verified in advance and limits imposed on what they can do on the system, such as ‘read only’. Add in AI to detect fraud and we may have an even more robust accounting system. Is it the end of the accountancy profession? The answer is probably not, but there could be some big changes.

How to use AI in your business

As a business owner you may have heard quite a bit about Artificial Intelligence and its benefits for business. However, you may not be aware that adding tools based on integrated machine learning, deep learning algorithms and other products is not as difficult as it sounds. Indeed, you may not even be aware that some of these are examples of AI.

Chatbots and virtual assistants

Have you visited a services website recently and had a box pop up offering to have a chat with you? Chatbots and virtual assistants are appearing on more and more websites. It isn’t difficult to find a chatbot service for you business and you can get a writer to provide you with a bespoke script that suits the tone and style of the rest of your website.

Online courses

It doesn’t have to cost you anything to get taught by the best. For example, Udacity offers a free Intro to AI course. Stanford University has a AI: Principles and Techniques course, and there are many others, including a Microsoft’s Cognitive Toolkit and MonkeyLearn’s ‘Gentle Guide to Machine Learning’.

Know what you want to do with AI

Once you’ve learnt the AI basics, it’s time to establish how AI can help your business. Think about how you can add AI capabilities to your existing products and services and look for ways n which AI can solve problems and add value.

Bring in the experts

Once you’ve identified some goals in your business where AI provides a valuable solution, it is probably time to organise consultations with AI experts. Setting up a pilot project that can be evaluated over a two to three months period, is one way to do it and bring in consultants to work with a small internal team. Once the pilot has been completed, you’ll be able to decide whether or not to take it forward for the long term.

Integrate AI in daily work routines

Once you have AI on board, make sure all workers have a tool to make AI part of their daily routine, rather than something that replaces it. AI scares some employees who feel threatened by it in the sense it might replace them, so it’s important to demonstrate that it is a help to them instead.

AI can really improve your chances of success and help teams to work more efficiently — so it’s time to get on board with the bots in business.