Why Most NLP Projects Fail and How to Prevent It

Why do NLP projects fail? Spoiler alert: Many reasons, including lack of proper guidance.

Natural Language Processing, or NLP, is a branch of artificial intelligence (AI) that deals with the interaction between computers and human languages. Google’s language translation program is one example of NLP. So are Grammarly, Siri, and Alexa.

Each NLP functions to interact with humans and machines. In theory, the concept is simple. In practice, it’s complicated—so complex it has a high failure rate.

Talk with a NLP specialist to start off right.

NLP Challenges

Simple failures are common.

For example, Google Translate is far from accurate. It can result in clunky sentences when translated from a foreign language to English.

Those using Siri or Alexa are sure to have had some laughing moments. NLP sometimes misunderstands simple commands.

GPS systems have led many down the wrong path. Major failures aren’t made public, so the number of instances is unknown, but there are some instances that are general knowledge.

What Makes NLP So Difficult?

Technology has made enormous advances in the past decade, and it shows no signs of slowing. It begs the question, “Why do most NLP projects fail?”

One reason is the human language itself. For NLP to work, it needs to understand both words and concepts to deliver the message.

The human language contains slang, idioms, plural words, and other ambiguities. These factors make it difficult for NLP to decipher and comprehend what people say. So much of what we speak is dependent on emotion to convey meaning, and it’s something that NLP can’t do.

NLP has another snag – and a rather large one. Computer scientists haven’t designed an algorithm that can beat those with questionable intentions, which has led to some of the most significant failures in Artificial Intelligence.

Finally, NLP is still new, which means few people understand it from top to bottom. That’s why finding a qualified consultant is essential. They can help turning your ideas into reality. Hire a NLP consultant now.

Facial Recognition Failures

Technophiles touted Apple’s iPhone X, which has a facial recognition feature, as the greatest thing since sliced bread. It uses the most technologically-advanced anti-spoofing networks.

It only took about a week for hackers to find a way around Apple’s so-called fail-proof AI by using 3-D printed masks. Some disputed the claim that hackers could unlock facial-recognition phones. Yet, it did highlight the vulnerabilities of facial recognition.

Apple wasn’t the only industry giant to have an AI egg on its face. Amazon had a major AI fail too. Their “Rekognition” system matched 28 members of Congress with the mugshots of criminals.

Also, both Toronto and MIT University researchers tried out the feature. They found that facial recognition systems performed with more accuracy on lighter-skinned faces. Despite the failures, Amazon continues to promote Rekognition.

Microsoft’s Tay

Microsoft’s hip, modern teenage AI called Tay, was going to take the Twitter world by storm. It did, but not in the way Microsoft planned.

Tay’s “playful” teen talk turned into a catastrophic failure. Trolls hacked the system and inserted raunchy racist and anti-Semitic tweets. The huge embarrassment caused Microsoft to lay Tay to rest.

Why Such A High Failure Rate and What To Do About It?

We’ve seen the language barriers between human and computer languages, but there are other contributing factors to Natural Language Processing and Artificial Intelligence failures.

The first is the lack of proper Research & Development. One of the reasons for failure is not conducting the appropriate research.

R&D can be a considerable expense, and cutting corners will only result in failure and cost more in the long-run. Investing in and committing to R&D is the best way to ensure you are market-ready.

It also provides fail-proof AI technology. Proper R&D will prevent the embarrassing failures of Amazon, Apple, and Microsoft.

What can we do about NLP failures? Here are some of the recommended actions:

• Ensure there is a Need – There has to be a need and interest in the technology. It may seem cutting-edge, but without customers and end-users, it’s a set-up for failure. It’s essential to find your market and place in society and not put the cart before the R&D horse.
• Create a Vision – When developing NLP and AI strategies, it’s crucial to have a vision and communicate it to employees often. Make sure employees know the short-term and long-term goals. This action puts them in sync and helps them understand their place in the project.



• Time Your Product Release– It’s always exciting to release a new product as soon as possible. Yet, many NLP’s fail because their release happened too fast. It’s essential to get a product out to the real-world with real-world users. But, if you have not carried out the appropriate tests, it’s a ticket for failure.

Conclusion

It’s almost impossible to have a fail-proof NLP. It’s a never-ending race to stay ahead of hackers. Still, with a focused strategy and proper R&D, you can prevent failure.

What is the benefit of implementing such an approach? It means not having to rebound from the embarrassment of a flawed product. It also means you can avoid the costs and loss of reputation associated with such an incident.