According to a Narrative Science survey, 38% of enterprises are already using AI technologies, and 62% will use those by 2018. Artificial intelligence is a hot topic: headlines are full of success stories about AI adoption. And this phenomenon is easy to explain: to use AI in real life, you need big data. Machine learning (ML) is the core of modern AI, and data is the raw material for ML. More affordable computing resources and cloud technologies allowed companies to cross the technical barriers that existed before and excel in ML. Companies are learning to deal with big data to gain deep business insights. Being 31 years in software engineering, we share six ideas that might be useful for those companies that are planning to reach new heights with the help of AI software.
1. AI needs big data
To perform the required task, an AI model must be trained on a huge and comprehensive data set. And the bigger this data set is, the better the result will be. (A typical training data set for a ML model may contain around millions or even billions of entries.) Today, software development services are free from technical limitations of the past and allow collecting, processing and analyzing big data even in real time.
2. Real AI is real time or near real time
Most of businesses need near real-time AI. Let’s look at the AI applications in different industries, all of which are real-time:
- Fraud detection
- Facial recognition
- Speech recognition (voice-to-text services)
- Translation (Google Translate, Skype Translator)
- Goods recommendation
- Intelligent digital personal assistants (Siri, Cortana)
- Autonomous vehicles
- Smart houses
3. Real AI needs cloud
Machine learning requires significant computing resources during the training stage, while the data processing stage is not so demanding. Earlier, this varying need in computing resources was a challenge for those who wanted to implement machine learning but were unwilling to make big one-time investments to buy servers powerful enough. The occurrence of cloud technology made it possible to satisfy this need easily. AI software development services can rely on either corporate or commercial cloud (like those provided by AWS, Microsoft Azure, etc.).
4. AI solution is far beyond ML algorithm implementation
Undoubtedly, algorithms are important in ML software development. However, many other elements highly influence the success:
Training data sets
In fact, training data set is key to success. If a company doesn’t have enough data or they are biased or of low quality, AI software is likely to make wrong decisions.
Machine learning can take two forms: supervised and unsupervised. In the first case, a ML model is supplied with both the training data and the desired output. In the second case, the output is unknown and the model has to find any trends and dependencies in the training data. So, a company has to choose the relevant approach to training.
Integration into daily business processes
To bring practical value, AI should become an integral part of daily business processes. To achieve this, AI software should be integrated with other corporate systems (those it takes new data from and those it translates the output to).
5. ML-based AI solution should be retrained frequently
Unfortunately, a ML-based AI solution is not something that is created once and for all. It’s important to understand that AI is not multitask. Unlike humans, it cannot be trained to play two different games. If AI learned to play chess, it can be extremely successful in doing this. But if this AI is to play Go, the system should be retrained and will be able to perform only the new task.
Machine learning model needs frequent retraining due to:
- External changes: Business environment is constantly changing: new competitors enter the market, new trends appear in the industry, etc. All this is likely to lead to changes in the tasks that a business has to solve.
- Internal changes: Any business is developing, it can expand to new markets or downsize, it can change the organizational structure or the structure of business processes; it can reconsider the corporate strategy, goals and KPIs. So, after some time a business might need to solve a different task with the help of the existing AI software.
6. ML solution should be verified and monitored
ML-based system sometimes makes mistakes. They may be minor (such as a wrong batch of recommended goods in ecommerce) or serious (such as an unnoticed fraud case in banking). However, even slight errors may have a significant impact, if they are frequent. For example, online customers are likely to abandon the retailer that seems unable to recognize and satisfy their needs. To avoid these negative consequences, the architecture of machine learning software should include a second control loop that will analyze AI decisions and identify errors. This "super intelligence" can be either human or other AI software module that observes AI level 1 decisions.
AI is a very promising trend that is gradually becoming a part of the contemporary world. With the rise of big data, available cloud technologies, AI has got on the radars of those businesses that would like to be innovative and stay ahead of competitors. We hope that the ideas we shared here will be helpful to the companies that are just paving their way through AI software development process.
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