The Ai Playbook — part 1: Main trends in artificial intelligence
Or…how Ai is going to change your business no matter what industry you operate in!
NOTE: this is the first episode of the AI Playbook series, if you want you can jump directly to part 2.
Artificial Intelligence (AI) is one of the most promising disciplines within Computer Science. It’s widely known that currently there is a big trend in its use for business development purposes.
The market for Artificial Intelligence solutions grew steadily throughout 2016 and has kept an elevated pace so far in 2017
In fact, Forrester Research predicted a more than 300% increase in AI investments for what’s left of this year compared to what was observed in 2016.
Companies that want to stay ahead of the digital transformation wave are attracted by the versatility of Artificial Intelligence algorithms. The same algorithms can be applied with little to no customizations to sectors so different as healthcare and autonomous driving technology.
To get a bit of perspective on how impactful this technology will be, we highly recommend watching this TED Talks from Kevin Kelly, Founder of Wired magazine:
The most popular AI product 20 years from now that everyone uses has not been invented yet. That means that you’re not late — Kevin Kelly
In this article, we will revise the multiple applications of Ai with the goal helping you navigate the next industrial revolution:
Natural Language Processing (NLP):
This refers to the ability of an AI to manipulate natural language to do “useful things”.
NLP research seeks to understand how humans understand languages and how to apply this knowledge to train AI systems to perform desired tasks. NLP has utter relevance in the way human and computer interact, and for this reason advances in this area deeply impact the bridge between human communication and digital data.
As you can appreciate from the video above, shot at Google in 2009, this technology has come a long way. Nowadays it used in a wide variety of contexts from chatbots to text translation and summarization.
A particularly useful application of Natural Language Processing algorithms is Automated Sentiment Analysis:
Sentiment Analysis is the use of natural language processing, statistics, and text analysis to extract, and identify the sentiment of text into positive, negative, or neutral categories.
This technique comes particularly handy when dealing with large datasets or dynamic social feeds like Facebook Newsfeed, as shown below:
For an introduction to Sentiment Analysis, we recommend readying The Beginners Guide To Sentiment Analysis, from Matt Kiser:
While for a detailed look at a real life application of Natural Language Processing, we recommend this article from our CEO Francesco Stasi:
Speech recognition.
Speech recognition technologies enables machines to respond correctly and reliably to human voices, and provide useful and valuable services based on requests expressed by the users using solely their voice.
Large investments are currently made to enable an artificial intelligence to transcribe and transform human speech into a useful format for computer applications.
Speech recognition has widely spread thanks to the popular use of the smartphone. It’s currently used in interactive voice response and virtual agents such as:
Virtual agents
This type of AI goes from conversational bots to advanced systems that can establish contacts with humans. It is currently used in customer service and support and as smart home managers like Google Home:
The most frequent model of bots is a Chatbot, a software capable of simulating a conversation with a person.
A chatbot is a conversational agent that interacts with users using natural language.
Precisely, because chatbots are able to converse with people, they are becoming increasingly present in messaging applications, as of today you can find them across messaging platforms, performing a variety of tasks.
Beware though, not all chatbots are created equal! Some are able to perform complex tasks leveraging their Natural Language Understating skills, while other can barely help you order a pizza.
If you have developed or planning to develop a chatbot, and you want to know how to make it “smart”, we suggest you ready this step-by-step guide from our co-chief ai scientist Kumar Shridhar:
If you are interested in the technical aspects chatbots, and you want to know how a smart chatbot comes to life, we highly recommend this article:
Natural Language Generation
This sub field of Artificial Intelligence research is the science of programming software to be able to write like a normal human being and eventually be able to pass the Turing Test.
Our Co-Chief Ai Scientist Kumar Shridhar has accumulated a lot of experience working with generative Ai algorithms applied to chatbots and virtual agents.
If you are curious about the Turing Test and how far we are from being able to talk to machines the same way we talk to our friends, we recommend reading this spot on article:
While if you want to dive deep into the topic and read about how a chatbot powered by a generative model Ai algorithm is created, we highly recommend reading another article Kumar Shridhar has written on the topic:
In the following weeks we’ll be releasing more articles covering more aspect of Ai, its application and the impact it can have on your business.
PART 2 is OUT!
Continue reading the next episode of the AI Playbook: A day in the life
BotSupply is a global decentralized network of talented AI scientists, bot engineers and creatives. Organizations, looking to leverage AI, machine learning and bots in their digital transformation journeys, become partners and tap into our network for custom built AI solutions and bots.
To ensure our partners continuously leverage cutting edge technology, we’re attracting and incentivising global AI talent to join our network with a unique revenue sharing model for the best performing solutions.
We call this co-creating artificial intelligence.
