There's a lot of misconceptions out there about AI and what it's capable of doing. It isn't magic, but it can produce amazing results for your business.
In short, AI makes systems capable of adapting and improving processes and situational responses over time. You've likely already had AI improve your everyday life with things like personalized movie suggestions based on your past viewing habits or when your phone's map app seems to know when you're about to drive home. When things just seem smart or personalized just for you, that's usually because of AI working behind the scenes. Now it's time to apply that same intelligence to your business.
Your business should care about AI because it can mean increased productivity for employees and a better overall experience. Imagine if your systems could anticipate employee needs, help them find exactly what they're looking for, or automate some of their busywork. That would translate to hours saved and happier, more productive employees.
Does AI really work?
Short answer: Yes. Long answer: AI is largely misunderstood (as explained below).
Artificial intelligence has been marketed both as technological magic and as software that should be adopted by every business for every business case. Neither of those notions is true. Like any technology, AI has an intended use case and -- when properly implemented -- it can produce staggering results. The trick is knowing when and how to employ AI so your business gets real return on its AI investment.
Where You Can Use AI:
- Classifying complex data: AI can self-generate criteria by which to reliably recognize and categorize text, images, documents, data files, and even software code.
- Automating complex tasks: AI can self-generate complicated logical flowcharts to take specific actions in response to a wide array of situations
- Predicting complex trends: AI can self-generate analytical models that can predict outcomes based on current information
The common thread here is that deep-learning algorithms can take the place of complex software rules engines. Rather than manually crafting a complex series of if-then-else statements, you can simply train AI to build its own rule sets, and then grade the results of those rules. The AI takes those grades, adjusts its algorithm to better identify "good" results, and tries again. This process is quickly repeated for a few cycles to train the AI to competence.
Once the AI learns to distinguish good results from bad on a consistent basis, you can put it in to action -- and that process is almost always more efficient than building a set of conditional statements or evaluation criteria yourself.
Some Implementations of AI:
- Image recognition: Google Image Search can now find images similar to the one you submit, even if you only have a fragment of an image. Facebook can recognize your face in your own and other people's photographs. No human team could so quickly or accurately make those connections. These systems work because those AIs have been trained to note distinguishing features in images and efficiently recognize them.
- Automated Trading: AI-management algorithms trade of stocks, bonds, and commodities at high speed for major investment group, reacting to market events -- and producing better returns -- than any human ever could. These AIs have been trained on years of profitable trains to recognize common factors of favorable market conditions, and respond accordingly.
- Natural Language Processing/Understanding (NLP/NLU): Machine translation like Google Translate and advanced chatbots like Talla's intelligent assistants may not be able to understand language as well as humans, but they can parse and transform that language into computational values -- like foreign language equivalents, or structured search queries -- faster than any person. These AIs have been trained on proper responses to, or translations of, numerous phrases and statements to find out how to best parse their meaning.
- Supervised Learning: Training programs that adapt content and instructional techniques based on the performance and preferences of the student. By observing what technique benefit student performance in what circumstances, these AIs can recognize cues the indicate when to shift instructional methods for a particular student.
AI is extremely powerful, but takes time to learn about your company, and it takes time for your employees to learn how to train and support AI systems. You can't throw extra money or staff at this problem and accelerate your AI learning curve, so you need to get started with artificial intelligence today. If you don't, your competitors will. The good news is, by adopting AI now, you gain an advantage over your competitors they can't easily overcome.
If you'd like help getting started on your AI journey, contact Talla today.