Artificial Intelligence -- especially machine learning and deep learning -- was everywhere at 2018 and do not anticipate the hype to die down over the following 12 months.
The hype will die eventually naturally, and AI will turn into another consistent thread in the tapestry of our own lives, just like the web, power, and gas did in times of yore.
However, for at least the next calendar year, and probably longer, anticipate astonishing breakthroughs as well as continued enthusiasm and hyperbole from commentators.
That is due to expectations of the modifications to business and society that AI claims (or even in some cases threatens) to bring around go beyond anything dreamed up through previous technological revolutions.
AI points towards a future where machines not only do each of the physical work because they have achieved since the industrial revolution but in addition, the "believing" function -- planning, strategizing and making conclusions.
The jury's still out on whether that will result in a glorious utopia, with people free to spend their lives after more meaningful pursuits, as opposed to on those which economic necessity dictates they dedicate their time to widespread unemployment and social unrest.
We likely won't arrive at either of those outcomes in 2019, however, it's a topic which will last to be hotly debated. In the meantime, here are five things that we can expect to happen:
1. AI Is Increasingly Becoming a Hot Topic For Global Politics
2018 has witnessed major world powers increasingly setting up fences to protect their national interests in regards to commerce and defense. Nowhere has this been more obvious than at the connection between the world's two AI superpowers, the US and China.
In the surface of tariffs and export limitations on products and services used to create AI enforced by the US Government, China has stepped up its attempts to turn into self-reliant in regards to development and research.
Chinese tech manufacturer Huawei announced plans to come up with its AI processing chips, reducing the demand for the nation's booming AI industry to rely on US manufacturers such as Intel and Nvidia.
At exactly the exact same time, Google has faced public criticism because of its apparent willingness to do business with Chinese technology companies (most with links to the Chinese government) while withdrawing (after pressure from its own employees) from arrangements to use US government agencies because of concerns its tech may be militarised.
With nationalist politics enjoying a resurgence, there are two apparent dangers here.
Primarily, this artificial intelligence technology might be adopted by authoritarian regimes to restrict freedoms, like the rights to privacy or free speech.
Second, that these tensions could compromise the spirit of collaboration between industrial and academic organizations across the world. This frame of open cooperation has been instrumental to the accelerated development and installation of AI technologies we see taking place today and putting up boundaries around a country's AI development is very likely to impede that progress. In particular, it's forecast to impede the development of common standards around AI and data, which may greatly increase the usefulness of AI.
2. AI & Automation Diving Deeper Into Each Sector
In 2018, companies began to get a firmer grip on the realities of what AI can and can not do. After spending the prior few years receiving their information in order and identifying places in which AI could bring rapid rewards, or neglect fast, large business is as a whole ready to proceed with proven initiatives, moving out of piloting and soft-launching to global deployment.
In financial services, huge real-time logs of thousands of trades per minute are routinely parsed by machine learning algorithms. Retailers are proficient at grabbing data through until receipts and loyalty programmes and feeding it to AI engines to work out how to improve at selling us things. Manufacturers use predictive technologies to know exactly what pressures machines can be placed under and when it's likely to break or fail.
In 2019 we will see growing assurance that this clever, predictive technology, augmented by learning it has picked up in its initial deployments, could be rolled out wholesale across all of a company's operations.
AI will branch out into support functions such as HR or maximizing supply chains, where choices around logistics, in addition to firing and hiring, will become increasingly informed by automation. AI options for managing legal and compliance issues are also very likely to be adopted. As these programs will often be fit-for-purpose across lots of organizations, they'll be offered as-a-service, offering bigger businesses a sting of the AI cherry, too.
We are also likely to see again in companies using their data to create new revenue streams. Building up large databases of transactions and client activity within its sector essentially lets any satisfactorily data-savvy business start to"Googlify" itself. Becoming a supply of data-as-a-service continues to be nominated for businesses like John Deere, that supplies analytics according to agricultural information to assist farmers to grow crops more efficiently. In 2019 more companies will adopt this strategy because they are to understand the worth of the info which they own.
3. "Transparent AI" Comes Into Picture
The adoption of AI across broader society -- especially when it involves coping with human information -- is mitigated by the"black box problem." Largely, its workings seem arcane and unfathomable without a comprehensive comprehension of what it's really doing.
To achieve its full potential AI needs to be reliable -- we need to know what it's doing with our information, why, and how it makes its decisions in regards to issues that affect our own lives. This is often tricky to communicate -- particularly as what makes AI especially useful is its ability to draw links and make inferences which may not be evident or may even seem counter-intuitive to us.
But building confidence in AI systems is not more or less reassuring the general public. Research and business will also profit from openness which exposes bias in data or algorithms. Reports have even found that employers are sometimes holding back from deploying AI because of fears they could face liabilities in the long run if the present tech is later judged to be unfair or unethical.
In 2019 we're most likely to observe a heightened emphasis on measures designed to raise the efficacy of AI. This year IBM unveiled technology developed to enhance the traceability of decisions into its AI OpenScale engineering. This concept gives real-time insights into not just what choices are being made, but they are being created, drawing connections between information that can be utilized, decision weighting and potential for bias in information.
Even the General Data Protection Regulation, place into actions across Europe this year, gives citizens some protection against decisions that have"lawful or some other important" impact on their own lives made exclusively by machines. Although it isn't yet a blisteringly hot political potato, its prominence in public discourse is likely to grow during 2019, further encouraging companies to make use of transparency.
4. More Jobs Will Likely Be Generated By AI Than Would Be Lost To It.
Like I mentioned in my introduction to this article, at the long-term its uncertain if the growth of the machines will result in human unemployment and social strife, a utopian workless prospective, or (probably more realistically) something in between.
For the following calendar year, at least, though, it appears it isn't going to be immediately problematic in this regard. Gartner forecasts that by the end of 2019, AI will probably be producing 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 bring a hit -- 2.3 million will be created. In particular, Gartner's report uncovers these could be centered on education, health care, and the public sector.
A possible catalyst for this disparity is the emphasis put on rolling out AI within a "bolstering" capacity in regards to deploying it in non-manual jobs. Warehouse workers and retail cashiers have regularly been replaced wholesale by automatic technology. Nevertheless, when it comes to doctors and lawyers, AI providers have made concerted efforts to exhibit their technologies as something which may work alongside human professionals, helping them with repetitive tasks while leaving the"last state" to them.
This means those businesses gain from the development in human tasks on the other hand -- those needed to deploy the technologies and train the workforce using it -- while keeping the professionals who carry out the actual work.
5. AI Assistant Will Surely Be Put To Use
AI is actually interwoven in our lives now, to the stage that most people don't give another thought to the fact that if they search Google, shop at Amazon or see Netflix, highly precise, AI-driven predictions are at work to create the experience flow.
A slightly more apparent awareness of engagement with robotic intelligence is about when we interact with AI assistants -- Siri, Alexa, or Google Assistant, for instance -- to help us make sense of their multitude of data resources available for us at the modern world.
In 2019 a lot more people that will use an AI assistant to organize our calendars, organize our journeys order a pizza. These solutions will become more and more useful because they learn to expect our behaviors better and comprehend our customs.
Data accumulated from users enables application designers to know exactly which attributes are supplying value, and which can be underused, perhaps consuming valuable resources (through bandwidth or reporting) that could be used elsewhere.
Consequently, functions that we really do want to use AI for -- for example ordering leftovers and food deliveries, and choosing restaurants to visit are becoming increasingly compact and accessible.
On top of this, AI assistants are designed to become increasingly efficient at understanding their human customers, as the natural language calculations used to encode language into computer-readable data, and vice versa is exposed to a growing number of information about the way we communicate.
It's evident that discussions between Alexa or Google Assistant and people may seem really stilted today. However, the rapid pace of comprehension in this field implies that, at the conclusion of 2019, we will be getting used to much more flowing and natural discourse with the machines we share our lives with.