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We’ve Already Surrendered

The march to the future probably doesn’t include us.

It’s a funny old world we live in. We think we have a grasp on this fragile thing called reality when, in actuality, nothing could be farther from the truth. People constantly talk about the Internet like it’s a thing. It’s not. It’s many things tied together, communicating globally, and rapidly evolving into something you may not recognize. Still, at its core, the Internet is a tool, like a hammer or a saw. It only can do what its user allows it to do. The Internet doesn’t automatically send you cat videos, you have to look for them. But that’s changing and you’re making it happen. More and more companies are using the Internet as a foundation for artificial intelligence. I’ll give you a fun instance. Recently Facebook users jumped on board for something called the ten-year challenge. Congratulations, you just helped create a perfect way for facial recognition software to build aging into its algorithms. And, bonus, since the postings were voluntary in a public forum, any company wishing to do so may use them. And several have. Google, for example, sells facial recognition software to police departments, and other government agencies. And they had access to your horrendous 2009 fashion choices. And your face, then and now. Existing software can easily remove goofy posts; like your dog then and now, or your cars and so on. The dataset they got is pretty indicative of how humans age. And that will be sold to all their customers.

Bernard Marr, over at Forbes, took a deep dive into how A/I wil be developing this year and the near future. This has been edited for brevity. Please click the link to read the whole thing.

AI increasingly becomes a matter of international politics
2018 has seen major world powers increasingly putting up fences to protect their national interests when it comes to trade and defense. Nowhere has this been more apparent than in the relationship between the world’s two AI superpowers, the US and China.

In the face of tariffs and export restrictions on goods and services used to create AI imposed by the US Government, China has stepped up its efforts to become self-reliant when it comes to research and development.

With nationalist politics enjoying a resurgence, there are two apparent dangers here.

Firstly, that artificial intelligence technology could be increasingly adopted by authoritarian regimes to restrict freedoms, such as the rights to privacy or free speech.

Secondly, that these tensions could compromise the spirit of cooperation between academic and industrial organizations across the world. This framework of open collaboration has been instrumental to the rapid development and deployment of AI technology we see taking place today and putting up borders around a nation’s AI development is likely to slow that progress. In particular, it is expected to slow the development of common standards around AI and data, which could greatly increase the usefulness of AI.

A Move Towards “Transparent AI”
The adoption of AI across wider society – particularly when it involves dealing with human data – is hindered by the “black box problem.” Mostly, its workings seem arcane and unfathomable without a thorough understanding of what it’s actually doing.

To achieve its full potential AI needs to be trusted – we need to know what it is doing with our data, why, and how it makes its decisions when it comes to issues that affect our lives. This is often difficult to convey – particularly as what makes AI particularly useful is its ability to draw connections and make inferences which may not be obvious or may even seem counter-intuitive to us.

But building trust in AI systems isn’t just about reassuring the public. Research and business will also benefit from openness which exposes bias in data or algorithms. Reports have even found that companies are sometimes holding back from deploying AI due to fears they may face liabilities in the future if current technology is later judged to be unfair or unethical.

AI and automation drilling deeper into every business
In 2018, companies began to get a firmer grip on the realities of what AI can and can’t do. After spending the previous few years getting their data in order and identifying areas where AI could bring quick rewards, or fail fast, big business is as a whole ready to move ahead with proven initiatives, moving from piloting and soft-launching to global deployment.

In financial services, vast real-time logs of thousands of transactions per second are routinely parsed by machine learning algorithms. Retailers are proficient at grabbing data through till receipts and loyalty programmes and feeding it into AI engines to work out how to get better at selling us things. Manufacturers use predictive technology to know precisely what stresses machinery can be put under and when it is likely to break down or fail.

In 2019 we’ll see growing confidence that this smart, predictive technology, bolstered by learnings it has picked up in its initial deployments, can be rolled out wholesale across all of a business’s operations.

More jobs will be created by AI than will be lost to it.
For the next year, at least, though, it seems it isn’t going to be immediately problematic in this regard. Gartner predicts that by the end of 2019, AI will be creating more jobs than it is taking.

While 1.8 million jobs will be lost to automation – with manufacturing in particular singled out as likely to take a hit – 2.3 million will be created. In particular, Gartner’s report finds, these could be focused on education, healthcare, and the public sector.

A likely driver for this disparity is the emphasis placed on rolling out AI in an “augmenting” capacity when it comes to deploying it in non-manual jobs. Warehouse workers and retail cashiers have often been replaced wholesale by automated technology. But when it comes to doctors and lawyers, AI service providers have made concerted effort to present their technology as something which can work alongside human professionals, assisting them with repetitive tasks while leaving the “final say” to them.

AI assistants will become truly useful
AI is genuinely interwoven into our lives now, to the point that most people don’t give a second thought to the fact that when they search Google, shop at Amazon or watch Netflix, highly precise, AI-driven predictions are at work to make the experience flow.

A slightly more apparent sense of engagement with robotic intelligence comes about when we interact with AI assistants – Siri, Alexa, or Google Assistant, for example – to help us make sense of the myriad of data sources available to us in the modern world.

Data gathered from users allows application designers to understand exactly which features are providing value, and which are underused, perhaps consuming valuable resources (through bandwidth or reporting) which could be better used elsewhere.

As a result, functions which we do want to use AI for – such as ordering taxis and food deliveries, and choosing restaurants to visit – are becoming increasingly streamlined and accessible.

On top of this, AI assistants are designed to become increasingly efficient at understanding their human users, as the natural language algorithms used to encode speech into computer-readable data, and vice versa is exposed to more and more information about how we communicate.

It’s evident that conversations between Alexa or Google Assistant and us can seem very stilted today. However, the rapid acceleration of understanding in this field means that, by the end of 2019, we will be getting used to far more natural and flowing discourse with the machines we share our lives with.

Alexa and Siri are perfect ways for AI to learn from humans. People speak to them, clarify meanings, explain allegories, and generally become helpful assistants to the machines. They are not here to make our lives easier, that’s just a bonus for now. They’re here to learn from us, about us, and then help “guide” us in preapproved desired directions.

That’s not fearful hyperbole, that’s a marketing platform. Ad Ext just listed 19 ways for AI to help ad agencies, and governments, limit, and focus, information users (that’s us) receive and the manner in which they receive it. If you thought people were living in bubbles before, you ain’t seen nothing yet.

Marketing automation allows companies to improve engagement and increase efficiency to grow revenue faster. It uses software to automate customer segmentation, customer data integration, and campaign management, and streamlines repetitive tasks, allowing strategic minds to get back to doing what they do best.

One of the leaders in this field is Adext AI, whose audience management platform can boost ad spend efficiency by up to +83% % in just 10 days. The software automates all the process of campaign management and optimization, making more than 480 daily adjustments per ad to super-optimize campaigns and managing budgets across multiple platforms and over 20 different demographic groups per ad.


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