One of the questions that we at the International Data Corporation are asked is what impact technologies like Artificial Intelligence (AI) will have on jobs. Where are there likely to be job opportunities in the future? Which jobs (or job functions) are most ripe for automation? What sectors are likely to be impacted first? The problem with these questions is that they misunderstand the size of the barriers in the way of system-wide automation: the question isn’t only about what’s technically feasible. It’s just as much a question of what’s legally, ethically, financially and politically possible.
That said, there are some guidelines that can be put in place. An obvious career path exists in being on the ‘other side of the code’, as it were – being the one who writes the code, who trains the machine, who cleans the data. But no serious commentator can leave the discussion there – too many people are simply not able to or have the desire to code. Put another way: where do the legal, financial, ethical, political and technical constraints on AI leave the most opportunity?
Firstly, AI (driven by machine learning techniques) is getting better at accomplishing a whole range of things – from recognising (and even creating) images, to processing and communicating natural language, completing forms and automating processes, fighting parking tickets, being better than the best Dota 2 players in the world and aiding in diagnosing diseases. Machines are exceptionally good at completing tasks in a repeatable manner, given enough data and/or enough training. Adding more tasks to the process, or attempting system-wide automation, requires more data and more training. This creates two constraints on the ability of machines to perform work:
- machine learning requires large amounts of (quality) data and;
- training machines requires a lot of time and effort (and therefore cost).
Let’s look at each of these in turn – and we’ll discuss how other considerations come into play along the way.
Speaking in the broadest possible terms, machines require large amounts of data to be trained to a level to meet or exceed human performance in a given task. This data enables the bot to learn how best to perform that task. Essentially, the data pool determines the output.
However, there are certain job categories which require knowledge of, and then subversion of, the data set – jobs where producing the same ‘best’ outcome would not be optimal. Particularly, these are jobs that are typically referred to as creative pursuits – design, brand, look and feel. To use a simple example: if pre-Apple, we trained a machine to design a computer, we would not have arrived at the iMac, and the look and feel of iOS would not become the predominant mobile interface.
This is not to say that machines cannot create things. We’ve recently seen several ML-trained machines on the internet that produce pictures of people (that don’t exist) – that is undoubtedly creation (of a particularly unnerving variety). The same is true of the AI that can produce music. But those models are trained to produce more of what we recognise as good. Because art is no science, a machine would likely have no better chance of producing a masterpiece than a human. And true innovation, in many instances, requires subverting the data set, not conforming to it.
Secondly, and perhaps more importantly, training AI requires time and money. Some actions are simply too expensive to automate. These tasks are either incredibly specialised, and therefore do not have enough data to support the development of a model, or very broad, which would require so much data that it will render the training of the machine economically unviable. There are also other challenges which may arise. At the IDC, we refer to the Scope of AI-Based Automation. In this scope:
- A task is the smallest possible unit of work performed on behalf of an activity.
- An activity is a collection of related tasks to be completed to achieve the objective.
- A process is a series of related activities that produce a specific output.
- A system (or an ecosystem) is a set of connected processes.
As we move up the stack from task to system, we find different obstacles. Let’s use the medical industry as an example to show how these constraints interact. Medical image interpretation bots, powered by neural networks, exhibit exceptionally high levels of accuracy in interpreting medical images. This is used to inform decisions which are ultimately made by a human – an outcome that is dictated by regulation. Here, even if we removed the regulation, those machines cannot automate the entire process of treating the patient. Activity reminders (such as when a patient should return for a check-up, or reminders to follow a drug schedule) can in part be automated, with ML applications checking patient past adherence patterns, but with ultimate decision-making by a doctor. Diagnosis and treatment are a process that is ultimately still the purview of humans. Doctors are expected to synthesize information from a variety of sources – from image interpretation machines to the patient’s adherence to the drug schedule – in order to deliver a diagnosis. This relationship is not only a result of a technicality – there are ethical, legal and trust reasons that dictate this outcome.
There is also an economic reason that dictates this outcome. The investment required to train a bot to synthesize all the required data for proper diagnosis and treatment is considerable. On the other end of the spectrum, when a patient’s circumstance requires a largely new, highly specialised or experimental surgery, a bot will unlikely have the data required to be sufficiently trained to perform the operation and even then, it would certainly require human oversight.
The economic point is a particularly important one. To automate the activity in a mine, for example, would require massive investment into what would conceivably be an army of robots. While this may be technically feasible, the costs of such automation likely outweigh the benefits, with replacement costs of robots running into the billions. As such, these jobs are unlikely to disappear in the medium term.
Thus, based on technical feasibility alone our medium-term jobs market seems to hold opportunity in the following areas: the hyper-specialised (for whom not enough data exists to automate), the jack-of-all-trades (for whom the data set is too large to economically automate), the true creative (who exists to subvert the data set) and finally, those whose job it is to use the data. However, it is not only technical feasibility that we should consider. Too often, the rhetoric would have you believe that the only thing stopping large scale automation is the sophistication of the models we have at our disposal, when in fact financial, regulatory, ethical, legal and political barriers are of equal if not greater importance. Understanding the interplay of each of these for a role in a company is the only way to divine the future of that role.
Spotify hits sweet spot
Streaming has shifted the music industry away from ownership and towards customer experience, writes ARTHUR GOLDSTUCK
Last week marked the end of the beginning of the streaming music revolution. Apple announced the closing of iTunes, the 18-year-old platform that helped shift the music industry from physical to digital. At its height, in 2014, close to a billion people were using it.
However, the business model was still based on traditional ownership of music. Users either converted their physical music into digital tracks, or bought songs from iTunes. Apple founder Steve Jobs said back in 2003, when the iPod music player was launched, that consumers “don’t want to rent their music… They don’t want subscriptions”.
History proved him spectacularly wrong, and when streaming subscriptions services like Spotify and Pandora began taking off, even as iTunes hit the 800-million user mark, the company launched Apple Music in a dramatic acknowledgment that subscriptions were the future. It was also an admission that iTunes, which had also become a download service for movies and TV shows, had become top-heavy and frustrating to use.
Apple’s late arrival in the streaming world has cost it: In January this year, Apple Music reached 50-million subscribers – exactly half the number paying monthly subs to Spotify.
Spotify took South African music by storm when it launched here in March 2018, thanks to close collaboration with local artists. It has a dedicated South African team that creates playlists for South Africans, in genres that appeal to local audiences. It also has a local ad sales team, and achieved early success with automotive brands like BMW and Mini using the platform extensively.
The company does not break down user statistics by country but, says Claudius Boller, managing director for Middle East and Africa, uptake exceeded all expectations.
“It’s been an amazing year,” he told Business Times. “Engagement in South Africa has crossed the world average. Users are extremely active, lean forward, and engage with playlists on a daily basis. We are not running many campaigns to move people from our free service to the Premium offering, but people do it right away.
“The metric we look at is how often and how long people use Spotify on average per day, and we have already seen those on premium subscriptions using Spotify much more than Facebook per day.”
The South African audience has another key differentiator, says Boller: “The market is extremely loyal. We know other music services have been in the market for many years. But when people make up their minds to try Spotify, they fall in love with it and continue to use it. The drop-off rate of people using our service is one of the lowest of all the markets in which Spotify operates.”
One of the secrets of Spotify’s success is the close relationship it builds with what it calls “the creative community” – both artists and labels.
“They are extra engaged, because of the data they are able to get. We give them a huge amount of data in a way that is very easy to digest. Through Spotify for Artists, they can see in real time how many listeners they have, their demographics, where they are listening, and where their audience is growing. If Jeremy Loops is doing very well in Australia, he can adjust where to promote his music and how to plan his touring schedule.
“We also use that data to work more closely with the creative community. We bring artists, labels and managers together for educational events so that they can get to know how to use the data. We give them practical advice, for example that they should release music on the same day on all platforms, including radio and streaming services, to maximise monetisation.”
Music entrepreneur Siya Metane agrees that audience data is one of the greatest benefits of streaming music. Better known as Slikour, founding member of the legendary hip hop group Skwatta Kamp, he now runs SlikourOnLife, an online urban music site and community with well over a million regular users. Understanding user trends has been at the heart of the growth of the platform, and he believes Spotify and its competitors add yet another dimension.
“The analytics that the streaming platforms provide give artists more insight of where their music is being consumed,” he says. “It is therefore giving the artists and their managers insight on where to invest nationally or globally. Such information has not been readily available to artists and managers before. Historically, everything was based on the physical purchase of a copy in a region – most of the time locally.”
But there is a downside, he says: “The cost of the streaming sacrifice is losing a whole R100 per album to a streaming company that pays you based on their pro rata plays on their service. Therefore only a few people can benefit. But streaming has definitely shifted the business from music alone to everything else music can influence.”
Both Vodacom and MTN have recognised the potential of streaming music to add value to their services, which are becoming increasingly commoditised. MTN late last year bought the local music streaming service Simfy Africa, and Vodacom in April this year launched its own streaming music service, called My Muze. The latter invites aspiring musicians to upload their music, with the possibility of being discovered and signed to a music label.
“The music industry has changed rapidly in recent times in that everything now lives digitally,” says Rehana Hassim, portfolio manager for music at Vodacom. “We also hope to attract new young consumers, to whom music remains one of the biggest passion points, providing various ways to engage with and consume the music they love.”
AI reveals SA domestic abuse trends
Digital abuse, infidelity, and alcohol abuse are emerging as common conversation topics between victims of domestic violence in South Africa and rAInbow, an artificial intelligence-powered smart companion.
Developed with funding partner, Sage Foundation, and social justice organisation, The Soul City Institute, rAInbow allows users to ‘chat’ to a non-human over Facebook Messenger. It provides a safe space for domestic violence victims to access information about their rights, support options, and where they can find help – in friendly, simple language.
When we launched rAInbow in November last year, we didn’t expect that it would facilitate over 200,000 conversations with 7,000 users – 150,000 of those within the first three months of launch. One of the reasons we believe Artificial Intelligence (AI) can fill a gap in victim support is because many victims are uncomfortable talking to another person about their experience – due largely to social and cultural taboos, embarrassment, and shame.
The data gathered from anonymised rAInbow conversations** providesinvaluable insight into this complex issue; insight that we can use to improve our communication and prevention strategies.
Digital abuse: Behind the screens
Around 30% of rAInbow users believe it’s acceptable for their partners to check their phones and to insist on knowing who they’re talking to at all times.
Yet this constitutes a form of verbal and/or emotional abuse because abusers exploit technology and social media to monitor, control, shame, stalk, harass, and intimidate their victims. In conversations with rAInbow, many victims reveal that they don’t know what constitutes digital abuse because they can’t recognise the signs.
You could be a victim of digital abuse if your partner demands to know your passwords and who you’re talking to, reads your messages, and dictates who you can be friends with on social media.
The bottom line is, when you’re in a relationship, all communication with your partner – be it digital or face-to-face – should be respectful. You should never feel pressured into doing anything you’re uncomfortable with.
Infidelity: Is cheating really abuse?
Infidelity emerged as one of the main challenges facing rAInbow users in abusive relationships. In such cases, the cheating partner usually blames you for his/her cheating, does it intentionally to hurt you, or threatens to cheat again to control you. Infidelity is often accompanied by lying, manipulation, and blame-shifting – all recognised abusive behaviours.
Technology has exacerbated the problem. It’s now easier to access dating sites, pornography, and chat platforms, facilitating behaviour like ‘sexting’, which some people may consider infidelity.
‘Alcohol made me do it’
Alcohol and drugs are common triggers for violent episodes, with rAInbow users saying their partners were more likely to lash out at them verbally or physically after they’d been drinking. While alcohol itself doesn’t cause domestic violence, it can aggravate already tense situations.
Alcohol impairs people’s judgement and behaviour, to the point where they may lose control and become aggressive, short-tempered, and abusive. In most situations, the abusive partner will blame the alcohol for their actions and may not remember what they did or said the next day. The abused partner, however, has to live with the memories and after-effects of the abuse.
In his State of the Nation Address earlier this year, President Cyril Ramaphosa said violence against women and children has reached “epidemic proportions” and that ending abuse would be made an urgent national priority. Corporates, NGOs, and ordinary citizens also have a responsibility to end the scourge.
Technology like rAInbow provides the vital information needed to start driving radical change – at policy and societal level. The conversations that rAInbow is having with users is making us think differently about how to approach this issue. It’s apparent that we need targeted, personalised education drives that help victims identify abuse and explain how and where to get help. It’s also apparent that there’s a strong need for information that can be accessed in a safe, anonymous, and non-judgemental space.
We need to use the aggregated data that’s available to us to make better decisions about action plans and strategies. Solutions like rAInbow can provide governments with the information they need to tackle abuse.
To find out how you can contribute to the rAInbow project, e-mail firstname.lastname@example.org.
** All conversational data is anonymised. It is used to improve rAInbow and help organisations make better decisions about where to focus their efforts to combat abuse.