Tag: AI Hype
Why AI? What Can AI do for me? When? How?
So many questions about AI! Considering that AI has become a revolution that has taken today’s business world by storm, it isn’t surprising that there are so many questions.
Most businesses can testify to the fact that Artificial Intelligence (AI) has improved the way their business operates. Businesses implement AI to increase efficiency, grow their revenue, and improve customer experience. Let’s explore the benefits of AI and get to some of the top questions about AI.
Read more: Artificial Intelligence: Taking the buzz out of the buzz word!
Importance Of AI Implementation In Business Today
According to the 2020 Global AI survey conducted by McKinsey & Co, 44% of the companies reported cost savings from AI adoption in their business. They also reported that AI adoption has decreased business units cost by at least 10% on average. Is this the only benefit of AI implementation? Not in the least. Here is why AI is so important.
1. Efficiency
AI handles manual tasks at a pace and scale that is beyond human ability. This allows workers to move to higher-value tasks that AI technology cannot handle.
Such a well-calculated balance helps businesses minimize the costs associated with mundane tasks while maximizing the talent of human capital.
2. Shorter business timeline
Businesses are moving faster in this digital age. However, AI empowers businesses to move faster as it enables shorter development cycles and reduces the time taken to move design to production output. In turn, this shortened timeline delivers better and more immediate ROI.
3. Improved customer service
Delivering a positive customer experience has become a determining factor between a successful and an unsuccessful way of doing business. Best customer service includes knowing the customer, their needs, and presenting customers what they need when they need it. That is exactly what AI can do constantly.
4. Near-instantaneous monitoring capabilities
AI’s capacity to process massive amounts of data can help businesses stay alert to issues, recommend action, and initiate a response. This prevents costly and disruptive breakdowns and thereby eliminates the cost of maintenance work. AI’s monitoring capabilities can also be effective in enterprise cybersecurity operations.
5. Error-free results
Human employees are prone to mistakes. By adding AI technologies businesses can ensure that errors are minimized as it can automate repetitive, rule-based tasks. This speeds up and enhances the entire process. Plus, it can be trained to improve and take on broader tasks.
Though AI has proven to be important for business success, some hesitate. Why? Let’s find out.
Read more: The future of Artificial Intelligence: A game-changer for industries
Why Do Businesses Still Hesitate?
Some decision-makers remain hesitant because they feel they are venturing into the unknown. Here are some additional reasons:
- Limited AI talent: Larger organizations are more likely to have the necessary technical skillset to understand the business case for AI investment. Some lag behind because they do not understand AI’s value proposition.
- Data issues: Insufficient quantity or quality of data is a common barrier to AI implementation.
- Concerns about responsible AI: Some have concerns about ethical breaches with AI.
- Concerns about AI investment: Many companies are concerned about moonshot investment.
Answering Top Burning Questions On AI Business Application
The rise of the popularity of AI welcomes several questions that must be answered to help businesses make informed decisions.
1. How does my business benefit from AI implementation?
AI cannot do everything. However, it can give your organization the ability to run automated tasks, pull reports and analyze data at the touch of a button. This can give you a strong competitive advantage. AI is capable of processing enormous amounts of data to unlock actionable insights.
2. When should I consider building an in-house AI-powered solution?
- When you are building something that differentiates your core product. If the project is not central to your business mission it probably isn’t the best use of your team’s time and resources.
- When you have a sizeable engineering infrastructure because you will need dedicated resources to build, maintain, train, and improve your solution.
- When no vendors can meet all your requirements. Here’s a tip: when evaluating a vendor, ensure to ask for customer references and case studies of companies where they successfully implemented their AI solutions. Using a vendor can often be faster, cheaper, and provide better results than what a company can build on its own.
3. How to evaluate if a company’s AI is “good?”
Here, “good” depends on human talent, data, algorithms, and computing power. In other words, it depends on the company’s ability to gather the best data and the best skill set to solve the problem at hand. So, it can be helpful to ask companies what is the quantity of data, compare the relevance of their data with yours, and how the system was built.
4. Why is AI relevant to customer service?
The businesses that skillfully weathered the storm caused by the pandemic provided their customers and employees with reassurance, stability, and easily accessible information whenever needed. Deploying bots on websites will increase the use of self-service and provide relief to phone or chat queues.
Read more: 9 Examples of Artificial Intelligence transforming Business Today!
5. How can AI be used for a quick win?
AI bots can handle high volume repetitive tasks as we have already discussed. These tasks include answering questions, scheduling appointments resetting passwords, and more. Automating these tasks can provide quick and economically substantial wins.
6. What are the pitfalls to avoid?
There have been designs that left customers adrift in a sea of endless menu options. To avoid repeating this pitfall, design your bot thoughtfully and within the context of the end-to-end customer journey. This must include a seamless path to agents where customers do not have to repeat the information.
7. How can Fingent help leverage the best of AI?
AI systems require training and Fingent understands that. We are here not just to implement AI solutions. We will train, monitor, and make sure that it works. We have an excellent track record with business cases showing the financial value of implementing AI.
Our experts at Fingent custom software development experts can develop solutions that quickly show financial benefits. We have invested in extensive training, which is why Fingent’s AI specialists can strategically plan data warehousing and cloud computing in their sleep!
We can build on a foundation of open-source software and develop an AI solution that fits your needs on commodity hardware. We can also choose a platform that is simple to deploy and manage. We assure you that Fingent can understand and support the complete lifecycle. Fingent can provide AI applications to solve the previously unsolvable. Let’s discuss your project.
Disclaimer: This is an opinion piece. The views expressed in this article are mine and does not represent my employer.
Smart, sentient machines! The latest (well, not really) hype! Look back a week or two, and think about the number of days you went without hearing about how AI is going to change your career, health, medicine, food, travel or whatever. Television, newspapers, and blogs remain constantly flooded with announcements about the imminent disruption <insert field here> that is going to witness due to using AI.
Let me show you some, ahem, examples.
We have here (in the order of increasing horror):
- AI-powered Air Conditioners
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AI-powered Washing Machines
Source – Gizmodo
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AI-powered Suitcases
Source – Indiegogo
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AI-powered Phones
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AI-powered Toilet
Source – The Verge
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AI-powered Underwear!
Okay, I made that last one up. But for a second there, you guys did believe me, right? RIGHT?
That is the sad state of affairs. We are all techies here, and might think “wait, WHAT?”. But the vast majority of the not so technical audience out there sees AI as magic. They see it as something beyond their cognitive ability to process and accept any BS branded as “AI-powered” without questions. Thus, we have this article!
Source – Mashable
So what is the truth with AI? If you dig deep enough, or if you peel off enough layers(pun intended), what is happening?
Before we move on to taking the buzz off of buzzwords, let’s look at some core concepts.
Related Read: Top Artificial Intelligence Trends to Watch Out for In 2019
What is AI?
From wiki, Artificial intelligence is intelligence demonstrated by machines. It is the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
But Really, What Is Artificial Intelligence?
IM[not so H]O, AI is just a buzzword. Really, it is just meaningless jargon. Okay, maybe not meaningless, but it’s still jargon. Don’t believe me? Let me give you some examples:
- Computers playing checkers and beating the best human players was considered AI. Until it was not when it was accomplished around 1994 by Chinook, the checkers-playing computer program.
- Computers playing chess and beating the best human players was considered AI. Until it was not when it was accomplished around 1997 when IBM’s Deep Blue defeated the then world champion, Garry Kasparov.
- Cruise control was considered AI. Until it was not when it started being available in production cars in 1990+(partial) and 2010+(full speed range).
- Automatic parking was considered AI. Until it was not when it started being available in production cars somewhere around 2006.
- Human speech recognition was considered AI. Until it was not when it started being available as Google Assistant, Cortana, Siri, etc. Now we have a real-time speech translation!
I could go on, there are quite a few examples of this phenomenon, formally known as(yes, it is so well known that it has a name) the AI effect [wiki].
So a much better definition of AI was put forth by Douglas Hofstadter.
“AI is whatever hasn’t been done yet.”
– Douglas Hofstadter
Just Computation
“Every time we figure out a piece of it, it stops being magical; we say, ‘Oh, that’s just a computation’.”
– Rodney Brooks
So, if it’s all just computation, why was it not, well, “computed” earlier?
Yes, computation, or rather, the capacity for computation is the key. A lot of problems were characterized as AI because, at the time, algorithms for solving that were not known yet, or because the resources to compute those were not available yet.
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Availability of Computation Power
Eg. Chess/other games, etc.
Moore’s law and the explosion in storage availability have played a major role in turning the tables. [It is important to note that the tables have not turned completely. Yet. There is so much more ground to cover.]
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Availability of Unbiased Data
Eg. Natural language processing (NLP).
Okay, now you may be thinking “Enough data was not available for speech recognition? This guy is full of BS”, but hear me out. With the explosion of social networks, so much content is created and made freely available that finding huge swaths of unbiased(this is the key here) voice/video of natural speech is available, which in turn has helped the advances in NLP.
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Availability of Infrastructure
I guess I don’t have to mention the improvement in internet speed that happened over the decade. This has accelerated content creation, real-time processing, etc.
So, What is All the Current Hype About?
The hype is not current. There has been huge interest around AI from the time it was first proposed around the 1950s. The sheer number of films about it tells us about how much.
But the current wave of hype and buzz surrounding AI comes from the recent advances made in, drumroll please, Machine Learning.
What is Machine Learning?
Machine learning is
- giving computers the ability to learn
- to find patterns in data
- from experience
- without explicit programming.
ML is essentially about classifying and predicting stuff.
The typical operation is something like:
- Take some data
- Learn patterns in the data
- When presented with new data, classify it for the best guess of what it probably is, based on the “learning” that happened in [2].
Related Read: Machine Learning- Deciphering the most Disruptive Innovation
Meh! So what is the big deal?
Once trained for one purpose, the same ML system can be reused(with additional training) to learn new concepts. This can be done without rewriting the code. Now that is a big deal.
Let’s look at a simple example: Classifying emails.
Traditional programming:
if the email contains "it's never a job, its always a career" then send to trash; if the email contains ... then ... if the email contains ... then ...
ML programs:
try to classify some emails; change self to reduce errors; repeat;
That was a two-minute primer on Machine Learning. So next time someone starts talking about Artificial I, I hope you feel the pang and say “Excuse me, I think you mean Machine Learning, not AI”.
Source – HubSpot