We’re sure you’ve heard a lot about artificial intelligence already. It’s one of the buzziest buzzwords in the IT industry. Put simply, artificial intelligence is the replication of human-like behaviors in software. But AI cannot become the revolutionary transformative force many predict it will be without machine learning algorithms, which work on finding patterns in datasets and act based on those patterns. It is machine learning algorithms that put the “intelligence” in artificial intelligence.
For instance, consider an algorithm that’s fed with thousands of images, each tagged as "this is a pup" or "this is not a pup." The algorithm is made to identify, then, from a new photo, whether it shows a pup or not. As the algorithm works on more data, it gets smarter, much like humans and performs its tasks with more accuracy and efficiency.
This was a very basic AI example, and certainly not a use-case that enterprises would be interested in. What are they interested in? We explore the answer to this question in this guide, by touching upon some high potential use cases of AI in enterprise settings.
Data security and privacy, arguably, is the biggest headache of CIOs these days. 2017 was the year when cybersecurity acquired demonic proportions and became a risk everyone acknowledged.
In 2014, Kaspersky Lab had said that it detected 325,000 new malware files per day. However, the reality is that new malware files tend to retain 90 to 92 percent of the original code, with just a few basic changes. This means that software that can identify the core pattern of malware can be expected to faithfully report new malware as well.
To this effect, there are already AI-powered cybersecurity startups working day and night at perfecting their technologies. The aim is to leverage machine learning algorithms to look for patterns in how a program works normally, and report anomalies, if and when found. This could bring in the much needed and highly coveted future-readiness into existing antimalware solutions.
Prediction of stock market movements is big (Gordon Gekko — your days are numbered!). Every day, thousands of people carefully watch stock prices fluctuate, waiting for the perfect moment to execute a trade, and potentially make a profit. However, human capacity for data processing is limited, and that’s where machine learning kicks in.
ML algorithms are getting closer to making accurate stock market predictions every day. Many market leaders from the stock trading industry are already using machine learning powered software to predict stock prices and make high-speed trades, in high volumes, based on the predictions and the actual trends.
Advanced statistical analyses and probability techniques come to the fore here. The key aspect here is that even if the probability of a trade is low, it can be executed in high volumes and at high speeds, making it profitable for investors. This will make AI a key focus for players in the financial markets.
Arguably, the most successful AI uses chatbots, which have created a positive impact already, and in many markets. Using natural language processing (NLP) along with machine learning, chatbots are able to automate interactions with end users.
These messaging apps can perform a variety of functions. For e-commerce websites, these bots can answer routinely asked queries from customers and can assist them in search for products they might be interested in. For any enterprise, chatbots can prove invaluable because of the “virtual assistance” they can offer to any employee or team.
Educational institutions, for instance, are already using chatbots on their websites and portals to answer questions from students. By taking care of a huge percentage of repetitive interactions, chatbots can free up a lot of time for customer support personnel, enabling them to perform more value-adding work. Britain's National Health Services, for instance, uses a chatbot to assist on its 111 helpline, wherein ML-powered messaging apps consult a large medical database and answers users’ queries.
Because there’s potentially no limit on the information-processing capabilities of machine learning algorithms, they can also spot more patterns than human counterparts, and do so much quicker. This makes AI a tremendous force for medical care because it can revolutionize diagnosis.
For instance, a study leveraged machine learning to scan through early mammography scans of women, and spotted 52 percent of cancers as much as one year in advance compared to an official diagnosis.
Early diagnosis in such cases can be lifesaving. Medecision is a leading startup in the space and has developed an algorithm that identifies eight variables to predict cases of avoidable hospitalization among patients with diabetes. Machine learning algorithms can work with a large number of microvariables and can be used to understand underlying risk factors for health hazards in large groups.
Machine learning algorithms are getting better every day at spotting cases of potentially suspicious financial activities. This has tremendous implications for the payment processing industry because AI can identify cases of fraudulent transactions, and raise timely alerts to not only prevent them, but also to help regulators and law enforcers to nab the perpetrators of online financial fraud.
PayPal, for instance, is already using AI to combat money laundering. It uses tools to compare millions of financial transactions and distinguishes between fraudulent and legitimate transactions.
In a recent IBM survey, 74 percent of respondents said they expected to see autonomous cars on the road by 2025. AI is empowering the concept of autonomous transportation, which can not only have significant implications for the transportation market, but also for manufacturing shop floors, warehouses, and shipping yards.
These systems can be made smart enough to report the potential need for replacement parts and machinery, can reduce the cases of on-road accidents and collisions, and add value with real-time time information about road conditions and traffic conditions to the user.
At its core, AI is all about replicating human intelligence and getting better at doing so with exposure to more and more datasets. Some of the use cases described above have already been realized at a preliminary level and soon enough, we can expect full-blown applications built around them.
Photo credit: Pexels
While Kubernetes is already the No. 1 container orchestration tool, a custom resource definition, or CRD, adds incredible custom features…
Here’s another in our popular Quick Tips series. This one focuses on PowerShell and how it can help you get…
Will Microsoft Teams dominate the market for real-time collaboration tools? And does integration with Office 365 ensure Redmond’s victory in…
There’s nothing more frustrating than applying an update or hotfix that breaks Exchange. One usual suspect in this scenario is…
There are good reasons to consolidate several VMs to a single virtual network. Here’s a script that will help you…
As cyberattacks grow in number and severity, organizations are embracing multifactor authentication. These startups are leaders in identity protection.