We spoke with the Delivery Director and Data Science & Engineering Lead for Encora Central & South America, Rodrigo Vargas, about Applied AI. The acceleration of Applied AI is 1 of 10 trends that Encora’s Innovation Leaders expect will help organizations respond to disruption in the new year and beyond.
The continuous increase in speed and capacity of CPUs and chips is driving AI models closer to mimicking reasoning and problem-solving skills. Read more about the applications and advantages of AI for organizations.
owe the rise of Applied AI?...To what do
When it comes to Applied AI, we are talking about how AI concepts and domains are applied to real-world problems to find real and practical solutions.
The rise in computer power and availability of AI in general has allowed us to address a broader range of problems that we could tackle before, thus developing new applications and uses for specific cases, like decision making, automation, and cost savings.
How are organizations benefit from AI?
How an organization benefits from using AI depends on the organization, but some examples include autonomous operations.
We can leverage technology such as Robotic Process Automation (RPA) and computer vision to identify patterns in the financial industry like default risks and fraud detection.
Computers don’t get tired, they don’t need a vacation, and they work 24/7. We can leverage their capacity for problem-solving to engage more with customers, for example, and to provide customer solutions for problems we haven’t seen before.
What direction will Applied in AI for next few years?
We are talking about narrow AI, and we identify narrow AI as the current state of technology. We can create AI models to resolve very specific problems but we cannot yet create models that can respond to more general problems like the human brain does. The human brain is versatile, capable of doing many complex things. AI, on the other hand, requires specific models for very specific use cases.
What are some common misconceptions about Applied AI?
People generally think of AI only when they see robots or self-driving cars. Again, AI is a broad term. It covers a large range of fields. One of which is called computer vision—the computer being able to identify what it is looking at and engage with those objects.
Another example is translation. People don’t generally think about AI when they see a translation tool but under the hood that’s using AI.
Misconceptions arise when people limit their recognition of AI to robots or chatbots. Their perception is limited to an interaction with another entity, whether it is virtual or whether it is physical. But AI goes beyond that and is being used in a wide variety of fields.
What are the different domains of AI?
AI has many fields, but the most common ones are: Computer vision – This is everything related to capturing what a human eye sees and being able to identify what is being seen by the computer. That’s object detection; Natural Language Processing (NLP) - It’s anything related to how humans communicate. There are more fields, but these are the big four.
What does it mean for AI to be responsible?
AI is a great tool used to accomplish many tasks. But there are concerns around how AI can replace human reasoning. There are a few challenges we need to address and, generally, when creating and training models, we need to make sure that the data that we feed those models removes the biases that would produce unexpected results.
Who is responsible for AI mistakes?
There are ongoing discussions about this because, generally, tech moves faster than legislation. Some would argue that the responsibility falls on people that trained the model but others would say the responsibility falls on the ones who deployed and used the model.
How can organizations protect their AI from malicious actors?
Cybersecurity is also a broad term. The difference here with AI is that the data itself can also impose a liability. So, how can companies protect it? First, the basic stuff, making sure that all aspects of cybersecurity are in place within the organization, like access to data and access to systems. To go a step further, deploy validation steps and verification steps for models that are being created so that they get properly tested, and properly verified without being used.
Please let us know if you would ever like to have a conversation with a client partner and/or one of our Innovation Leaders about accelerating next-generation product engineering within your organization.
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