All too often, the terms Artificial Intelligence (AI) and Machine Learning (ML) are used interchangeably. This improper use of the terminology can contribute to a significant amount of confusion around an already complex field of computer science. As such, there is a greater risk of propagating mistruths about what either terms refer to; particularly when they are being explained to an untrained audience.
In general, AI is a term used to refer to a broader field of science that explores how computers can be engineered to behave similarly to human intelligence. While somewhat vague by definition, this is down to the fact that AI is a constantly growing and evolving field – of which, Machine Learning, is one of the many subfields that stems from it. To better understand how one relates to the other, it is important to dig a little deeper into the history of AI.
A Brief History of the Origins of AI and Its Journey to Present Day
The term Artificial Intelligence has been in use since the Dartmouth Workshop organized by Professor John McCarthy in 1956. His aim was to create a working group on this, as-of-then, little-explored field. A deeper interest on the subject and promise of AI began to develop and with it, financial support to investigate further also grew.
However, in 1974, the economic backing began to diminish and over the next six years gave rise to the first ‘AI Winter’. This period of freezing on funding has been linked to a general sense of disappointment with the rendered results, given the initial hype raised. AI research made a comeback in the early 80s but faced other Winter periods later on due to similar circumstances. Although progress was being made, it wasn’t happening at a fast enough rate to keep up with the public’s expectations. More winter periods are expected in the future due to this habitual occurrence of anticipation build-up being met with slow progress.
Tom M. Mitchell, Professor and Former Chair of the Machine Learning Department at Carnegie Mellon, defines ML best as “the study of computer algorithms that improve automatically through experience.”. Despite having been around for years, this specific branch of AI is enjoying more applied success in recent times. This can be chalked up to the fact that, nowadays, we have access to the large data sets that provide the opportunity for the algorithms to prove their value. TranscribeMe is able to develop and constantly improve their Automatic Speech Recognition models to offer clients the highest accuracy of quality transcription services thanks to ML technology.
Understanding the Main Differences Between AI and ML
Thanks to increasing developments in the field of AI, there will surely be more vocabulary to familiarize ourselves with. Doing your best to understand the current lingo is key to making sure your business is accurately associated with a specific AI-related technology, thus avoiding any mixup.
Whereas some misuse of the terminology may be intentional for marketing purposes, in most cases it is due to a lack of understanding of the diverse technologies and their functionalities. The importance behind highlighting and clearing up any confusion around these terms is to help demystify the different functions and applications of AI so we can make the most of this useful technology as it increasingly becomes a part of our personal and professional daily routines.
To shed some light on the matter, here is a brief breakdown of the main characteristic differences between AI and ML:
| AI | ML |
| The objective with AI is to increase the chances of success | The objective with ML is to increase accuracy |
| It works as a computer program that does smart work e.g. Siri, Alexa, etc. | It involves creating self-learning algorithms that learn from simple data input |
| It simulates the decision-making process of human intelligence to solve a complex problem | It gains experience from running through datasets to maximize a machine’s performance in relation to a specific task |
| AI strives to find the optimal solution to a problem | ML comes up with a single solution for the specific question asked, whether or not it is optimal |
| This field is about cultivating intelligence or wisdom | This subfield generates knowledge |
| Adapted from the following article. | |
At TranscribeMe, we offer highly accurate, human-verified transcription services which are used to train high precision speech recognition models for a wide range of use cases. One example of this is our work with Voicea and their virtual assistant, EVA, that is used to capture and record every important detail in professional meetings.
Our already powerful platform is continually boosted through the knowledge gained with every new file we transcribe. With this, our automated speech recognition (ASR) models are constantly improving and giving better results each time. We offer fully-customized AI training data for your speech recognition systems, which includes:
- Custom annotations
- Complete full verbatim transcription
- A multiple step review process
- Capabilities to include customized meta-tags
- Multiple language support (currently available in 10+ languages & dialects)
- And much more…
Bring your company into the future by incorporating speech analytics in your operations with TranscribeMe’s Automatic Speech Recognition (ASR) Solution. To find out how our ASR models can be tailored to work for your organization, get in touch with a representative today!
