What Are the Advantages of Artificial Intelligence? from Mila's blog

Machines have become a lot smarter in the last 50 years. Artificial intelligence (AI), once a radical concept among computer scientists in the 1950s, now has more practical applications in our day-to-day lives than some people realize. AI powered technologies now play a vital role in marketing, finance, data analysis, healthcare, and much more. 

AI systems predict what you’re typing into a search engine. They serve up personalized ads based on your purchase and browsing history. They recommend new albums and playlists curated from your favorite songs. Advanced AI systems can even help doctors more quickly diagnose and treat illnesses in patients.

Innovations in the AI space have yielded benefits across many industries. Processes are more efficient, forecasts are more accurate, and convenient technologies are more widely available than ever. In the first part of our series on the benefits and disadvantages of AI, we’ll explore a few of the advantages that artificial intelligence systems present and how they make our lives easier. 

One of the greatest advantages of AI systems is that they enable humans to be more efficient. AI can be leveraged to perform small, repetitive tasks faster, or it can be used to complete much larger, more complex tasks. Regardless of its application, AI systems are unbound by human limitations and will never get tired.

And despite what you’ve seen in the movies, AI isn’t always glamorous. In fact, it’s often used to perform mundane, monotonous, time-consuming tasks that humans wouldn’t find particularly enjoyable. For instance, insurance companies use AI to process claims faster and at a higher volume than a human could, freeing up time for humans to focus on more important matters. Programs can also read and mimic a user’s keystrokes to automate dull work like data entry. Conversely, when operating alongside other technologies, AI can also process large amounts of complicated data and deliver actionable insights efficiently and accurately.

While AI is frequently leveraged to automate entire tasks, it also has many practical applications for improving human workflows. Deep learning technologies like natural language processing (NLP) and automated speech recognition (ASR) have transformed the way people work in professions like education, media and entertainment, and law enforcement. By converting the spoken word to text, ASR technology can automatically produce transcripts of any audio and video. 

Speech recognition systems like Rev are already shockingly accurate, and you can expect that to continue as the science improves and the algorithms are trained further. ASR allows lawyers to get transcripts of a three-hour deposition almost instantly. Filmmakers and video producers leverage automated transcripts to manage their media assets and edit their content faster. Police officers can quickly find key evidence from body camera footage thanks to automated transcription. These are just a few of the ways AI can streamline workflows.

As we’ve already mentioned, AI systems don’t have to deal with fatigue. Humans, on the other hand, get tired. The human brain can only focus on one task for so long before that focus starts to slip. When people get tired, they’re more likely to make poor decisions and become vulnerable to mistakes. Repetitive jobs can be particularly prone to human error — when a task is repetitive, it’s easier for humans to lose concentration. But AI systems don’t have to focus — they’re programmed for their function. And for those assigned tasks, AI systems eliminate the risk of human error, producing a more accurate result. 

Modern businesses are swimming in data, but are they getting the most out of it? While manual data analysis is a time consuming enterprise, AI systems can process and analyze massive amounts of data at remarkable speed. AI systems can quickly find relevant information, identify trends, make decisions, and offer recommendations based on your historical data. For instance, algorithms can quickly analyze the effectiveness of marketing materials, identify customer preferences, and offer actionable insights based on those customer behaviors. 

This bandwidth for deeper data analysis allows business leaders to make better, more informed decisions to benefit their operations. Take classification models, for instance. These machine learning algorithms attempt to draw a conclusion based on historical training data. After processing that training data, the model will then categorize, or “label,” new data. 

Businesses leverage classification models when analyzing customer attrition, assessing anticipated churn rate. These models can generate a list of customers at risk of churning, enabling a business to proactively take preventative actions. Classification models also help marketers with lead scoring, examining a customer’s compatibility with a given product or service.

According to the Bureau of Labor Statistics, Americans work an average of 8.8 hours a day. Whether we’re productive that whole time is an entirely different story. But machines don’t take breaks for coffee. They don’t step away from their desks to catch up with colleagues. And they definitely don’t pack up and go home at 5 p.m. Digital assistance solutions like chatbots are available to take customer inquiries no matter the time of day.