Have humans finally turned machines into living beings?
Google engineer Blake Lemoine's claim that the tech giant's AI technology is showing signs of consciousness has triggered fierce debates within the scientific community. However, the notion that Lambda is a sentient AI was quickly debunked by Google. It claims that Lambda is nothing more than a highly advanced chatbot trained to interact naturally in conversations.
While Google has shattered any preconceived notion of a fictional sentient AI, critics remain concerned about the ethical implications of such possibilities. Amidst the never-ending debate, businesses are better served by understanding what powers AI and how to leverage them to advance their purposes.
Artificial intelligence (AI) is a science that allows computer systems to perform tasks and rigorously solve problems in ways that mimic human intelligence. One subdiscipline of AI is machine learning, which requires repeatedly training the machine learning algorithm with sample data. Google Lambda is one of the many AI-based chatbot that has gone through rigorous training.
The AI algorithm focuses on making the best decisions upon evaluating existing datasets and parameters. It evaluates the decision and continues to refine itself through self-learning. With continuous training, AI-based systems demonstrate problem-solving capabilities for its specifically assigned task that can match its human creators.
Complex AI algorithms use deep learning and neural networks, allowing machine learning to take place on a multiple-layered architecture. Deep learning trains the algorithm with unstructured data to learn by experience and performs better than conventional training methods.
The question of whether AI can evolve and develop consciousness has no easy answers.
One of such prerequisites is the machine must at least reach the level of artificial superficial intelligence whereby it can spontaneously solve problems by its own judgement and creed. Existing AI technologies are nowhere near to achieving artificial general intelligence, and instead can only perform tasks assigned to it.
Scientists are equally divided if complex algorithms that train machines to process language and converse like humans can also develop self-awareness. Such discussions are also compounded by the fact that consciousness is hard to define and even harder to prove.
For an AI to prove sentience, it must go beyond mimicking humans when making decisions. This is no easy feat as AI can be biased when trained with incomplete or unrepresented datasets. Consciousness involves developing self-awareness, emotions and other cognitive capabilities related to values, self-goals and creativity. As intelligent as Lambda is, it has yet to demonstrate signs of a sentient AI. Instead, it is fairer to state that AI is a digital extension of human intelligence with the narrow ability to perform specific tasks well.
Prolonged debates on AI sentiency can be academically satisfying, but we must be grounded by the fact that we are still far from realising artificial general intelligence. Instead, the discussion shall be centered on incorporating AI in industrial and commercial applications to improve productivity, efficiency and mitigate risks, where the goal is for pervasive and omnipresent AI.
AI can transform how people search for information on the internet by improving the search results' precision, speed and relevance. Major search engines rely on AI algorithms to improve the user search experience particularly avoiding negative bias when displaying the search result.
Machine learning capabilities are also prominent in voice and image search. The advanced features allow search engines to understand subtle nuances in speech and identify images' vital characteristics before translating them into queries. For example, Google Assistant, Microsoft Cortana, Amazon Alexa and Baidu leverages AI speech recognition technology to respond naturally to human’s queries.
Machine-driven industries benefit from AI as the self-learning algorithm helps operators identify specific patterns in production data. AI technologies allow them to recognize and detect product defects at a fraction of a time, outperforming human vision to improve machine performance.
Incorporating AI robotics into production lines can also enhance workspace safety. Designed to work alongside human operators, these intelligent robots continuously monitor the immediate surrounding and compare the data with pre-determined parameters to indicate potentially hazardous situations.
Advancement in AI spurs deep learning technologies, which involve structuring the AI algorithm to mimic the human brain. Deep learning systems can process unstructured data and simulate humans' thought processes. Autonomous car development is driven by deep learning, which processes data from various sources in real-time to provide a safer driving experience.
Besides powering systems that operate without human intervention, deep learning allows software to consolidate and process data on a greater scale. For example, weather prediction software leverages the deep neural network to analyze and accurately predict the weather for the entire globe. London-based AI company, DeepMind, has successfully developed AI software that accurately predicts rainfall within the next couple of hours.
Researchers often grapple with the complexity of medical data analysis as they attempt to devise new drugs, treatments or biomedical technologies. They use AI-powered software to uncover complex data and provide much-needed clarity in research. For example, Moderna uses AI to speed up genome sequencing when developing the COVID-19 vaccine. Meanwhile, predictive analytics allows researchers to identify trial candidates and streamline real-time data monitoring.
Medical AI signals the dawn of personalized medicine. Instead of offering generic healthcare solutions, doctors can devise treatments according to the patient's behaviour, medical history, genetics and environment using AI technologies. AI robotics help medical professionals perform remote diagnosis, post-treatment care and surgical assistance.
Biometric scanners with built-in AI modules boast high-precision features that match multiple identifying parameters with massive databases, thanks to AI components. Cutting-edge facial recognition software uses AI and deep neural networks to compare images taken from different angles accurately. Law enforcement agencies use biometric software to compare missing children’s photos with AI-generated adult facial composite in cases that have remain unsolved for years.
AI-driven facial recognition has grown tremendously over the years. Social media giant, Facebook, introduced the DeepFace in 2014. The facial recognition program is capable of matching two different face images belonging to the same individual with 97.25% accuracy. Google managed to do slightly better with FaceNet in the following year, with an impressive accuracy of 99.63%.
Security firms invest in advanced AI-powered biometric trackers capable of monitoring digital footprints such as mouse moments, keystrokes and mobile device motions. AI analytics prove to be a reliable defense mechanism that blocks fraudulent attempts to bypass security measures by uncovering abnormal patterns in the collected data.
Organizations' digital assets can be better protected by introducing AI as part of cybersecurity defense strategies. Software that utilises machine learning capability helps security specialists to filter false alerts from possible threats efficiently.
By relying on automated AI-respond mechanisms, organizations can mitigate potential cybersecurity incidences and ensure service continuity. AI helps the security team increase security resilience by analyzing system vulnerabilities and crafting a suitable threat response plan.
For example, CrowdStrike offers a next-gen AI-based detection system that mitigates threats with user and entity behavioral analytics. Meanwhile, Cynet uses AI to provide autonomous threat management solutions for companies that do not have a cybersecurity team. DarkTrace’s Enterprise Immune System compares network activities with normalized baseline established via unsupervised machine learning.
At present, the world is unlikely to witness a fictional sentient AI brought to life. However, the benefits of AI for businesses and consumers are rooted in making it pervasive and omnipresent in industries and our daily lives. Along with 5G, AI presents new opportunities in the next-generation technology ecosystem, including extended reality, edge IoT and cloud analytics.
Prepare your business for AI by collaborating with a strategic 5G partner.
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