Look behind the curtain: Don’t be dazzled by claims of ‘artificial intelligence’
We are presently living in an age of “artificial intelligence” — but not how the companies selling “AI” would have you believe. According to Silicon Valley, machines are rapidly surpassing human performance on a variety of tasks from mundane, but well-defined and useful ones like automatic transcription to much vaguer skills like “reading comprehension” and “visual understanding.” According to some, these skills even represent rapid progress toward “Artificial General Intelligence,” or systems which are capable of learning new skills on their own.
Given these grand and ultimately false claims, we need media coverage that holds tech companies to account. Far too often, what we get instead is breathless “gee whiz” reporting, even in venerable publications like The New York Times.
If the media helped us cut through the hype, what would we see? We’d see that what gets called “AI” is in fact pattern recognition systems that process unfathomable amounts of data using enormous amounts of computing resources. These systems then probabilistically reproduce the patterns they observe, to varying degrees of reliability and usefulness, but always guided by the training data. For automatic transcription of several varieties of English, the machine can map waveforms to spelling but will get tripped up with newly prominent names of products, people or places. In translating from Turkish to English, machines will map the gender-neutral Turkish pronoun “o” to “he” if the predicate “is a doctor” and “she” if it’s “a nurse,” because those are the patterns more prominent in the training data.
In both automatic transcription and machine translation, the pattern matching is at least close to what we want, if we are careful to understand and account for the failure modes as we use the technology. Bigger problems arise when people devise systems that purport to do such things as infer mental health diagnoses from voices or “criminality” from pictures of people’s faces: These things aren’t possible.
However, it is always possible to create a computer system that gives the expected type of output (mental health diagnosis, criminality score) for an input (voice recording, photo). The system won’t always be wrong. Sometimes we might have independent information that allows us to decide that it’s right, other times it will give output that is plausible, if unverifiable. But even when the answers seem right for most of the test cases, that doesn’t mean that the system is actually doing the impossible. It can provide answers we deem “correct” by chance, based on spurious correlations in the data set, or because we are too generous in our interpretation of its outputs.
Importantly, if the people deploying a system believe it is performing the task (no matter how ill-defined), then the outputs of “AI” systems will be used to make decisions that affect real people’s lives.
Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.
The Nastel i2M Platform provides:
- Secure self-service configuration management with auditing for governance & compliance
- Message management for Application Development, Test, & Support
- Real-time performance monitoring, alerting, and remediation
- Business transaction tracking and IT message tracing
- AIOps and APM
- Automation for CI/CD DevOps
- Analytics for root cause analysis & Management Information (MI)
- Integration with ITSM/SIEM solutions including ServiceNow, Splunk, & AppDynamics