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.

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.

Quantum computers are almost here

A lot of people are very excited by the idea of quantum computers. There’s a theory that they are going to solve all the worst problems that the world faces, such as poverty. They are going to bring a revolution, yet as Dan Robitski writes at Hacker Noon, “we’re not really sure what a quantum computer will even look like, but boy are we excited.”

Now, the National Science Foundation (NSF) in the USA has plans to take what seems like a pipe dream and make them a reality in its research labs. It’s going to cost a lot of money, but the NSF doesn’t mind paying.

In August, the federal agency announced the Software-Tailored Architecture for Quantum co-design (STAQ) project in which a band of physicists, engineers, computer scientists and others from six universities, including MIT and Duke, will start a five-year project at a cost of $15 million. The aim is to build the world’s first practical quantum computer.

This quantum computer will go beyond proof-of-concept and will have to outperform the classical computer. What is the difference between the two? There are two key points here:

  • Classic computers use bits that are either 0 or 1, whereas a quantum computer uses qubits that can be both 0 and 1.
  • Qubits transfer information via quantum logic gates that route the qubits via photons and ions.

The problem facing the programme is that the researchers and scientists need to build a quantum computer that is actually practical, and this will require work on both hardware and software.

They will need to figure out how to make qubits less prone to error and how to streamline responses to our queries. It is likely that they will have to build automated tools that optimise the way algorithms are “mapped onto specific hardware in an effort to solve both these issues at the same time. Can they do it all in five years? They seem confident, then we will see just how revolutionary quantum computers are!

If you’re interested in quantum computers, you might enjoy reading this interview with Kenneth Brown of Duke University. He is the engineer in charge of the programme