Probably you mayn’t have come across this statistics. According to Narrative Science survey, 38% of enterprises already benefited from using AI technologies and the remaining 62% will use those by 2018. Artificial intelligence is a trending topic. There is a full of success stories around it.
In fact, AI is not an independently built technology. It needs big data to extract right pieces of information from millions of data sets. It needs machine learning to create algorithms. AI needs deep learning to understand human’s behavioural patterns. When it comes to machine learning, expensive computing resources and cloud technologies were barrier to build ML solutions. But today that is far away from the adoptability. On the other hand, big data technologies have gone easier to be built and gain deep business insights. We can see many mobile app development companies USA, and big data companies USA who widened their service portfolio with AI services to leverage trends.
If you’re looking forward to build an AI application, you require to consider 6 essential things that I am going to talk about here.
Things to consider before developing an AI application
1. AI requires big data
As said above, AI requires big data. In fact, AI must be trained on a vast and comprehensive data set. The result goes better when the data set is bigger enough. Every training data set for a machine learning model consists of millions or even billions of entries.
2. Most AI applications are real-time
Most of enterprises need near real-time AI. But most AI applications innovated today are real-time. Here is a greatly compiled list of real-time AI applications in different industries:
- Facial recognition
- Speech recognition (voice-to-text)
- Translation (Google Translate)
- Product recommendation
- Fraud detection (in real-time)
- Personal assistants (Siri, Cortana)
- Autonomous vehicles
Near real-time AI applications:
- Medical AI assistants
- Fraud detection systems
- Sales and marketing automation tools
You decide which AI you need to empower your enterprise.
3. AI needs cloud
Machine learning is everything about feeding the right piece of information to the machine. In fact, it is same as training a newly joined employee. So there is a need of substantial computing resources during the training stage. Earlier it was a big challenge even for big organizations to buy servers. But the cloud technology made the life a lot easier for even startups. If you’re looking to build an AI application, seriously think about this as well.
4. AI application is not ML algorithm
Undoubtedly, machine learning powered algorithm is the backbone of any AI application. But that is not just enough. There are many other elements that influence the success of any AI application:
Training data sets
If you don’t have filtered data sets or your data sets are of low quality, your AI application is likely to make frequently wrong decisions. You probably have observed such wrong results if you ever used chatbots or any digital personal assistant.
Training machines can be in two forms, either supervised training or unsupervised training. In supervised training, machine is trained with both the training data and the intended output. In unsupervised training, output is left to machine, which the machine (model) has to decide from the trends and correlative data included in the inputted data.
Integration into daily business processes
You can expect the best performance from your AI application only when it understood your business well. To generate practical values, your application should become an integral part of daily business processes. To get done it, think about integrating your AI application with other corporate systems in the organization.
5. You must retrain ML-powered AI applications
AI applications are not like built once and deployed for years. Unless until it is super artificial intelligence, machines need continual training whether it needs to hone its skill or play a different task. Chatbots kind of applications sharpen their skills depending on the conversations they involved before. But we can’t expect accuracy in the outputs. The effort to hone their skills creates better value.
Machine learning model requires continual retraining due to:
- Internal changes – market extension, changed organizational structure, restructured business processes, changed corporate strategy, goals and KPIs
- External changes – new trends, and new competitors
6. ML solution should be verified and monitored
ML-based systems are not free from mistakes. Sometime they may recommend irrelevant products (minor) or not notice fraud case in banking (major). So there is a need of verification and monitoring of every new case and every new activity respectively.
Artificial intelligence is already a buzz word. If you’re serious enough to achieve competitive advantage, you should build a serious AI solution. Collaborating with expert AI development companies is a fantabulous benefit if you’re new to this future technology.
Hari Krishna is a well-versed content writer working in FuGenX Technologies, an emerging mobile app development company India. He likes to write on technology, start-ups and latest technological innovations that people like to know and share with others.