Now more than ever, we must access an endless range of technologies that are purported to improve our lives. Of course, not all of them are equal in their ability to make any meaningful impact. There’s a difference between an impressive innovation and something with actual practical utility.
You needn’t look further than the Consumer Electronics Show (CES) in Las Vegas to see just how many “solutions” miss the mark and are often left in the wastebin of innovation, usually due to an ineffective business model.
Where does artificial intelligence (AI) stand in this context? It’s clear that recent advances in machine learning have led to a great deal of optimism – and fear – around the technology, from bots that outplay human champions, to virtual writers that spin together articles in seconds, to algorithms that can detect diseases years in advance.
How much of this is making it to the mainstream and how much is merely short-lived hype? How is machine learning changing the way companies operate? Let’s begin by understanding what exactly machine learning is before exploring its current applications in the corporate landscape.
Table of Contents
1. What is Machine Learning?
In short, machine learning is a branch of AI-driven by data analysis that automates specific processes.
It’s based on the notion that computers can learn from data, identify patterns, and use the information to make decisions – all with little to no human intervention. Put, the idea is that AI can learn without explicitly being programmed to.
We can create an example using the Photos app on Apple devices.
It uses artificial intelligence and machine learning to streamline the tagging process. When you tag someone’s face in one of your pictures, the program searches through the rest of your library to find the same person and automatically attach the associated tag.
The technologies in question essentially allowed the app to “learn” who your friend is and their name. Most machine learning algorithms operate in this manner.
They use mathematical models to predict outcomes, whether it’s figuring out who’s in your photo, what the value of your stocks will be in the future or the probability that your loan application will be accepted.
As these predictions are developed and made more precise, they can be implemented in previously considered extraordinarily challenging or even impossible scenarios. We’ve already seen this with realistic renderings of pictures of people that never existed and the development of cures for dangerous viruses.
2. How Will Machine Learning Change Business?
There are, namely, two ways that machine learning will change the way organisations operate.
At lower levels, the technology can take over predictive and menial tasks that employees initially performed, saving time and improving efficiency. This can manifest in countless ways.
For instance, radiologists may use artificial neural networks to review more x-ray slides, while customer support services can send quick responses.
But we already realise the potential to go far beyond this. Prediction machines have become so accurate and reliable that they can change how companies do things in more complex ways.
For instance, Amazon is using machine learning algorithms to recommend products to shoppers. The aim is to provide more relevant content while benefiting the customer with a more convenient experience.
Online streaming services use similar technologies to offer music, movies, and videos that users are more interested in.
You can read more about how popular streaming platforms are using machine learning here and how technology is being used to power autonomous vehicles.
Going back to Amazon, the precision of predictive technologies can reach another, even higher level. In doing so, it might change their entire business model. Currently, the online shopping giant uses a shop-then-ship method. You make a purchase, and they deliver your products to your door as quickly as possible.
Another approach is to switch the model around to ship-then-shop. Amazon uses machine learning to determine what you need and sends the products your way. If you need them, you pay, and if not, the products are returned at the company’s expense. Of course, this only works if the prediction model is accurate enough.
3. Who Uses Machine Learning Today?
In that day and time, the vast majority of large businesses in industries that work with enormous amounts of data have already recognised and leveraged the value of machine learning technology set’s take a look at six key sectors where machine learning is currently being used.
i. Financial Services
Banks, loan providers, and other financial industry organisations use machine learning technology in various ways. The two primary purposes are to identify insights from data and to prevent fraud. The former can reveal hidden investment opportunities, while the latter can identify high-risk clients and avoid cybersecurity threats before an attack occurs.
Some of the various primary uses of machine learning can be found in the healthcare sector. This comes in the form of wearable devices and sensors capable of assessing patient data in real-time. Machine learning can also assist medical experts in analysing data to improve diagnosis and treatment.
EnergHere’sre’s another industry where the uses for machine learning are nearly endless and ever-expanding. It can help find and implement new energy sources, analyse minerals in the ground, predict equipment failure, and streamlining distribution to improve efficiency and reduce expenses.
Even among government agencies, which are often viewed as behind-the-times, machine learning is utilised in various areas. The technology makes particular sense here as governments have access to vast amounts of data, which can save citizens money, detect fraud, and prevent identity theft.
As we touched on earlier, online retail is especially conducive to AI and machine learning. Websites can use it to make accurate recommendations on previous purchases by analysing your shopping history. Retailers also rely on machine learning to implement marketing campaigns, optimise prices, and gain customer data insights.
The utility of machine learning in transportation ranges far and wide. The transportation industry relies on making routes more efficient and foreseeing potential needs to increase profitability. Data analysis is fundamental to delivery companies, which illustrates an intersection between the industry and retail.
These are just a fraction of the uses for machine learning in some industries where the technologies are valued.
4. What are the Current Themes in Machine Learning
Let’set’s end off with a few key trends shaping the machine learning landscape today?
i. Processing Power
Artificial intelligence and machine learning have only started gaining mainstream popularity in recent years, mainly due to the need for many logic engines spread across a large amount of high-speed, dense flash memory. Only recently have the demands for neural net-based deep learning been met by the required computing power.
It was found that combining both CPUs (central processing units) and GPUs (graphics processing unit) can improve the speed of deep learning and similar analytics methods.
Another reason for the recent boom in AI is the widespread availability of capable cloud technology. Cloud computing is instrumental in democratising AI by enabling companies to access the technology and machine learning systems’ necessary computing capacity.
Now more than ever before, organisations survive on the ability to adequately protect their private data and mitigate the risk of cyberattacks. The traditional prevention-based approach to this problem has been replaced with a more active detection of threats than machine learning.
iv. Behavioural Analytics
With an endless onslaught of security alerts, businesses might struggle to discern real threats from harmless anomaliIt’sIt’s not uncommon for systems to detect breaches days or even weeks if it’s too late. Behavioural analytics helps by using various techniques, namely machine learning, to detect threats in large volumes of data more reliably.
v. Online Fraud
Another security issue that affects consumers just as much as organisations is online fraud, which often remains under the radar for months before inevitably causing significant financial and reputational damage. Modern online fraud detection systems use a combination of machine learning and behavioural analytics and identity authentication.
While these technologies improve fraud detection systems’ efficacy, they also help cybercriminals develop more advanced tools. This has sparked a never-ending race to stay ahead of the enemy.
vi. Advertising | Machine Learning Business
Among the main challenges that marketers face is the tighter regulation of the digital advertising sector. Factors like data privacy and protection, along with copyright, fake news, and tax avoidance, are all prime for code. Machine learning-driven tools are becoming available to assist advertisers in creating effective campaigns.
The solutions include responsive search advertisements that use machine learning to distribute content and automatic adjustment of bids to optimise ad performance on video streaming platforThere’sre’s no clear end to the applications for AI and machine learning in the business world. Only time will tell what the future brings for these technologies and their impact on society as a whole.
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