One of the biggest conversations for our future involves Artificial Intelligence. According to Tiempo, Artificial intelligence is poised to be one of the biggest things to hit the technology industry (and many other industries) in the coming years. Renowned author James Barrat says A.I. is and will be "Our Final Invention". If that is in fact true then it is our responsibility as humans to recognize and work towards any potential problems we may face in the future.
Let's explore Artificial Intelligence’s Biggest Challenges
Bias
Bias is one of the biggest challenges facing AI. Some data may be considered "cold hard facts" like 1 + 1 = 2 however, bias from here on out will always exist when you explore the depths to which AI might be used. Forbes India explains the inherent bias in data, “An inherent problem with AI systems is that they are only as good – or as bad – as the data they are trained on. Bad data is often laced with racial, gender, communal or ethnic biases. Proprietary algorithms are used to determine who’s called for a job interview, who’s granted bail, or whose loan is sanctioned. If the bias lurking in the algorithms that make vital decisions goes unrecognized, it could lead to unethical and unfair consequences…In the future, such biases will probably be more accentuated, as many AI recruiting systems will continue to be trained using bad data. Hence, the need of the hour is to train these systems with unbiased data and develop algorithms that can be easily explained. Microsoft is developing a tool that can automatically identify bias in a series of AI algorithms.”
Computing Power
The tech industry has faced computing power challenges in the past. The computing power necessary to process massive volumes of data to build an AI system, and utilizing techniques like deep learning, is unlike any other computing power challenge that has been previously faced in the tech industry. Obtaining and funding that level of computing power can be challenging for businesses, particularly startups.
Integrating AI
Seamlessly transitioning to AI is more complicated than adding plugins to a website or creating a Visual Basic for Applications (VBA) enhanced Excel Workbook. One must ensure that current programs are compatible with AI requirements, and that AI is implemented into these programs without stopping current output. The AI interface needs to be set up in a way that infrastructure, data storage, and data input are considered, and that the output is not negatively affected. Additionally, once this is completed, ensure that all personnel are trained on the new system.
Collecting and Utilizing Relevant Data
For an organization to successfully implement AI strategies and programs, they must have a base set of data and maintain a constant source of relevant data to ensure that AI can be useful in their selected industry. Data can be collected in many ways in formats such as text, audio, images, and videos. The wide range of platforms to collect this data adds to the challenges of artificial intelligence (observational, experimental, simulation, and derived). In order to be successful, all this data must be integrated in a manner that the AI can understand and transform into useful results.
Man Power
Because AI is an emerging technology, there are few who possess the skills or training necessary for artificial intelligence development. Due to the fact that lack of knowledge is a significant problem in the software development industry, many companies will need to allocate additional budget towards artificial intelligence development training, or the hiring of artificial intelligence development specialists.
Implementation Strategies
Artificial intelligence can transform almost every industry, but one of the major challenges of artificial intelligence is the lack of a clear implementation strategy. In order to be successful a strategic approach needs to be established while implementing AI. This includes identifying areas that need improvement, setting objectives with clearly defined benefits, and ensuring a continuous process improvement feedback loop. Managers of Artificial Intelligent systems will need to have a solid understanding of current AI technologies, their possibilities and limitations, as well as keeping up to date on the current challenges with AI. This will enable organizations to identify areas that can be improved by AI.
Legal Issues
One of the newest challenges of artificial intelligence include the recent legal concerns being raised that organizations need to be wary of AI. If AI is collecting sensitive data, it might be in violation of state or federal laws, even if the information is not harmless by itself but sensitive when collected together. Even if it’s not illegal, organizations need to be careful of any perceived impact that might negatively affect their organization. If the data collected is perceived by the public as violating their data privacy, the improvement for the organization might not be worth the potential public relations backlash.
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Source: Tiempo