AI
Discussing the techniques to handle AI and Big data problems. Poor data quality highlights the need to develop smart AI algorithms that can manage and handle the data collection expertly. But what if those algorithms fail to predict, assemble, and manage the data the way it’s required? What if the biggest technology – Artificial Intelligence seems a bit unreliable to trust?
With the outbreak of COVID-19, issues regarding AI algorithms surfaced more often. Some confusion and problems were going on lately but the outbreak highlighted some of the more serious ones.
AI has always regarded as the ultimate solution to handling the Big Data but it seems like it’s not performing the way it is expected too during the pandemic especially. So, here is a look at the common AI and Big Data problems and the ways to fix them efficiently. Read on!
Issue 1 – Big data problems and AI
With the outbreak, the concept of normal life has been hugely altered. Now the way one can predict human behavior has also been altered as well. During this time, the way the AI algorithm is responding to the changing environment is troublesome.
To make the algorithms work, the programmer has to insert certain data related to the environment and human physic to make the algorithm predict human behavior. Now it seems that the algorithms are left in the past because the environment is changing fast and unpredictably.
The British mathematician, David Barber stated about AI and its shaky prediction at the CogX 2020 conference.
“The AI won’t tell you when it isn’t confident about the accuracy of its prediction and needs of a human to come. There are many uncertainties in these symptoms. So, it’s important that the AI can alert the human when it is not confident about its decision.”
For instance, in the banking sector AI algorithms used to decide the lending cases where they have to evaluate the credit scores and individual’s income to make him credible for the campaign. However, as things are changing during the pandemic. It is becoming difficult for the algorithms to come up with a proper decision based on the current income scenarios.
See Also: Facial Recognition Technology
Issue 2 – Big data problems and AI
As the pie chart clearly shows only 9% of the time is spent mining the data a huge proportion of the time. Which is around 60% is invested in organizing and cleaning the data by the organizations that are indeed a problem not to be ignored.
AI algorithms do make work obsolete for human workers but its cleaning and management require a lot of skilled expertise. The time spends on data mining should be greater than the one spends on cleaning and organizing it. It can double productivity and make the use of data more prosperous.
The researchers and programmers are not quite sure if they can retro-clean the AI data to fulfill the needs of AI use. It’s just that if you cannot validate the AI data back to its respective source how can one be sure if it’s clean or not? An example of AI and its data cleaning issue is stated below:
For instance, AI being the blood-pool of Facebook was causing problems related to the spread of fake news, so the authorities hired Jérôme Pesenti, who is the former head of IBM’s Big Data group to handle the matter. New AI infrastructure gets designed and incorporated into the system that can filter out the fake news related to the COVID-19 pandemic and better measures were taken to keep the information clean.
Issue 3 – Big data problems and AI
“In particular areas machines have superhuman performance, but in terms of general intelligence we’re not even close to a rat.” stated by LeCum
You may be fascinated by how brilliant AI algorithms defeated the World champion with its Go Playing Computer. But as you go into the depth of the matter you will realize that that success was structured and programmed. The algorithms interpreted the data points and acted on it, which leads to the defeat of the champion. However, when it’s about the general application, the AI algorithms have a long way to go.
See Also: Types of Data Storage for your Business
As the use of AI begins to enter many different fields from AI for fashion to eCommerce many tech giants hunted for better ways to use it in their business. The Case of AI answering and responding to human queries made the point clearer as stated below:
For instance, Kevin Scott celebrated in a LinkedIn post the success of his AI algorithm with the ability to read, answer the queries about Wikipedia pages of the readers. However, the machine struggled during its practical assessment. There were many jerks, pauses, errors, and delays associated with the AI performance. Upon which he stated that,
“We are still a long way from computers being able to read and comprehend general text in the same way that humans can.”
Wrap Up
Surely, AI and its algorithms need a lot of improvisation to be able to replace humans at present. Even if in some fields it manages to replace the human workforce.
There must be a need to hire a separate department of programmers. Who can constantly update and improvise the data indexed into the algorithms concerning the changing environment?
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