Voice technology that power devices like Amazon’s Alexa and Google Home is the next frontier for emerging tech companies.

Facebook’s announcement to launch ParlAI recently only intensified the industry’s ambition to reach the ultimate goal of having meaningful conversations with computers by voice.

But let’s hold onto our horses; we’re not there yet.

At MediaMonks, we’re receiving increasing requests from brands eager to explore this emerging utility, and at the same time, we’re working with engineering and product teams to understand exactly what the technology can do. As it stands, we still have leaps to make, but one thing is sure: Voice-activated tech is set to get smarter, and fast.

Recently, I was speaking with a top executive at a leading global consumer products company. While watching TV, he saw an interesting ad for a product similar to his own. This prompted him to test Alexa. He asked what the best brand for the product category was, and Alexa promptly responded with a list of competitors. Later, one offered to send him a sample, and another listed best prices. This goes to show that while we may not yet be having meaningful conversations, voice-activated tech is rising and so are opportunities for brands to embrace it.

The Good, the Bad and the Promising

A recent study shows the U.S. market for voice-activated assistants has grown nearly 130 percent since 2016.

Today, Amazon Echo (Alexa) and Google Home — which differ from Apple’s Siri and Google Now in that they’re independent, stationary devices — dominate the market. Their main function is to provide a “smarter home” by calling up music, reminding you of your agenda, and even answering trivia questions.

One of the biggest benefits of voice-activated tech is that it saves time. Speaking is more natural than writing, and because you don’t have to take out your phone, it’s faster. It’s also more accessible for those who, for one reason or another, aren’t able to use keyboards or screens.

Perhaps. But this feature is still prone to error. When many people are speaking close to a device at once, it tends to have difficulty actually hearing the activation phrase. In the end, if you have to repeat your request again and again, it can be more time-consuming than just walking over to flip a switch.

There’s also the issue of privacy to consider. Burger King’s recent TV ad using “OK, Google” is a prime example of this. The ad used the wake word “OK, Google” to prompt devices to describe its burgers, but within hours of release — and hilarious edits to the Whopper Wikipedia page — the commercial was pulled. The widespread coverage of this ad highlighted the fact that voice technology is still new for many, and the idea of anyone, or anything, listening in on people is unnerving.

These issues are mere glitches, however. The biggest challenge is that although we’ve created processes that allow computers to get better at translation, voice recognition, and speech synthesis, most computers still don’t understand the meaning of language.

And, research confirms the average person is struggling to find value adopting this emerging tech trend in their daily lives.

Brands Should Prepare for Tomorrow, Starting Today

The list of current limitations is long. Despite these drawbacks, advances in machine learning mean that computers are getting better at recognizing what people are saying. We’re not there yet, but Zuckerberg’s ambition of AI that understands conversational speech may not be far off.

In 2011, the global voice recognition market was valued at nearly 47 billion. Six year later, that figure has more than doubled to 113 billion. Along with Facebook’s new announced investment, there’s a rush to accelerate the transition from speech recognition to natural language processing at scale. Once this is achieved, Zuckerberg’s wish for computers to have more sophisticated conversations will become possible.

Brands can start preparing for this new frontier today. As my earlier example of Alexa demonstrates, soon more and more consumers will be turning to these products to compare options and make purchases. Brands need to anticipate this change now by integrating these devices in their ecommerce and marketing strategies. In much the same way online shopping transformed the brick and mortar retail experience, voice activation technology will take this to the next level.

Each day, the promise of meaningful conversation and results-oriented solutions provided by humans interfacing with computers is evolving. Let’s all continue to explore and contribute to these technologies as they become smarter and more meaningful…one word at a time.

This article originally appeared on VentureBeat on June 26, 2017.

Machine learning’s potential to reveal hidden information in data is amazing and it’s already a part of our daily lives.

However, as MediaMonks’ Technical Director Michiel Brinkers reflects in this post, its possibilities are still largely unexplored. Here he gives his view on what machine learning can mean for creative digital production going forward and the challenges that need to be overcome to get there –

They’re coming for us, or are they?

For those unfamiliar with machine learning (ML), it’s a type of algorithm which learns from patterns in data and then, based on what it learned, can recognise and predict similar patterns in new data. It’s an application of artificial intelligence.

The technology is evolving at a phenomenal pace and every few weeks a new API, paper or prototype is released, continually raising the bar. Currently, the bulk of the research in ML is being developed by the big tech companies such as Google, IBM, Amazon and Microsoft. All have been researching ML for years and recently Facebook has also been making enormous strides.

A lot of work is also being done by universities, independent research groups and individual developers. And through the collaboration of Partnership on AI, as well as open source projects, emerging tech companies are working alongside all varieties of engineers and creative technologists to advance the field.

So, what’s stopping us?

The first challenge we face in is that models, which contain all the information needed to classify data, are difficult to create. It takes thousands of classified input data points to create a model, and unless a client already has the right data available, it’s hard to come by. The good news however is that creating a completely new model isn’t always necessary. Using an existing system, such as the Google Vision API, may in fact be preferable from a time, cost, and features perspective. So, smart choices in the creative approach can usually cover up any shortcomings.

The second challenge is fixing bugs. ML can be a bit of a black box, with even scientists often only able to speculate at its inner workings. This means that fixing bugs is a major challenge and vastly unlike regular programming. It’s not a matter of simply changing a few lines of code, but requires engaging in a whole new cycle of trial, error and discovery to develop the right model.

The last challenge is that we need to learn how to design with ML in mind. While the success rates for output can be impressive, it’s not perfect. For example, human lipreading has a tested success rate of around 20%, while ML has a 50% success rate. So, if we want to build a campaign around this, we need to come up with a solution for the remaining 50%.

With machine learning we need to take the user on a journey and help them appreciate what’s going on. It shouldn’t be a binary experience if it’s to be impactful, and there needs to be room for error built into the concept. So, to solve this we have to be creative with the tools we have, and come up with smart fallback solutions which don’t break user engagement.

Looking to the future

For marketers, ML can be integrated with a multitude of touchpoints, including Social, DOOH screens, platforms and campaign sites, and can play a powerful role that influences the consumer decision-making journey.

Some possibilities include using object recognition so a brand can recognise types of products from competitors and show the user an equivalent product in its range. Or, using voice recognition to develop conversational UI or voice controlled websites.

For digital agencies opportunities lie in creatively implementing available applied machine learning solutions, such as the Google Vision API, TensorFlow, IBM Watson, Microsoft Cognitive Services and API.ai,. We’re seeing tremendous potential for clients and users, and are particularly excited about:

• Object recognition: Google Vision, Cloud Video Intelligence, segmentation of images, helping the blind see photos.
• Voice recognition: home automation systems, websites and mobile apps.
• Language processing: Natural Language Understanding and emotion detection in text.
• Gesture recognition
• Manipulating Neural Networks: Deep Dream, Google Arts & Culture Experiments.

Through trial, error and deployment, here at MediaMonks we’re working with lead researchers to explore ML’s potential and how we can build solutions at scale. We’re quickly moving from a position of “we think this is possible” to “we know this is possible”.

Can we build serendipitous experiences through the use of predictive algorithms? Can we recognize fashion trends based on Instagram and what people are wearing in the street? Can we create awareness by detecting cyberbullying? Can we recognize endangered species being sold at a market?

Client briefs that were once out of reach are suddenly becoming a reality and we are excited. Machine learning will enable us — and others — to craft the most captivating digital work seen in years. Just watch this space…

This article originally appeared on HuffPost on May 15, 2017.