AI Engineering Through the Hype

In defining solutions, it is important to recognise there is no single, one-size-fits all AI Algorithm. What is becoming increasingly important, when AI is applied, is that the source of the data, the methods used to create, and train the algorithms are transparent and explainable. Detecting and mitigating any bias in algorithm design is an ongoing effort that we at IBM, and all companies advancing AI, have an obligation to address proactively.

To empower companies to take control of their systems, IBM released AI Fairness360, a comprehensive open-source toolkit of metrics to detect unwanted bias in datasets and ML models, and state-of-the-art algorithms to mitigate such bias. The toolkit, available to the open source community, helps developers and data scientists examine and repair bias in AI models throughout the entire AI application lifestyle.

In addition, IBM recently launched ‘Everyday Ethics for Artificial Intelligence’ a first-of-its-kind resource for AI designers and developers, articulating practical best practices captured from experts in fields such as AI, engineering, ethics, and philosophy. It describes five ethical principles that AI developers must consider throughout the design process.

With the skyrocketing growth of data as a driver for the AI era, responsible AI also extends to being responsible when collecting, storing, managing, or processing data. With around 80 percent of resources available to any AI project focused on data definition, preparations, and cleansing, it is of no surprise that the demand for the role and function of the data scientist has dramatically outpaced the supply with 63 percent of executives’ interviews as part of a recent Institute of business Value study: identifying the lack of in-house talent needed to build and implement AI solutions.

The current developments within AI represent an incredible opportunity. The winners in this domain will be those who understand how to successfully apply AI to real world problems. That means selecting the right problem, building solutions instead of algorithms, focusing on the data, and ensuring its responsible and ethical adoption.

One example of this is start up Hello Tractor. Working with IBM scientists in Africa, engineers are building a blockchain-enabled and AI-based decision support platform enabling farmers in sub-Saharan Africa to access tractor services on demand. With more than 60 percent of crops managed manually and food demand increasing due to population growth averaging 11 million per year, it is an unsustainable model. Using ML and IoT, Hello Tractor, will allow tractor fleet owners to view and manage fleet utilisation and predictive maintenance as well as forecast future tractor utilizations based on history, real-time weather, and remote sensing satellite data. This concept of an agricultural digital wallet will enable capturing, tracking, and instant sharing of data, while creating end-to-end trust and transparency for all the parties involved across the agribusiness value chain.

AI requires new skills for new kinds of work. To realise the opportunities this new era presents, we must look to build new partnerships to modernise skills, education for students, invest in re-skilling, and in developing the next generation of engineers with the abilities to fulfil our AI ambitions.

See Also: Top IBM Solution Companies In Europe