Intelligent cloud and AI solutions allow enterprises to go way beyond automation. Intelligent systems listen, learn, and act, thereby creating real value in healthcare, finance, and manufacturing, as well as in day-to-day lives.
Modern technological innovations have gone beyond the boundaries of core automation. Currently, it is expected with broad anticipation that such systems have native intelligence, allowing them to learn, adapt their functionalities, and make their own choices. Examples of such intelligent systems are voice-activated assistants, advanced cameras capable of recognizing abnormal behavior, and medical programs capable of suggesting alternative treatments.
The core building blocks of such systems involve two core transformational technologies, Artificial Intelligence (AI) and Cloud Computing. AI supports the system’s capacity for learning and evolution, similar to the developmental processes found in the human mind, while Cloud Computing provides the underpinning infrastructure required for the global deployment and operation of such systems. Together, the technologies underpin the realization of more complex, context-sensitive, and individualized experiences for businesses, as well as operational efficiencies.
Features of a cognitive computing system
The complex system performs tasks beyond the mere fulfillment of predetermined protocols. It collects information, does analysis, derives conclusions, uses logic, and continually improves its methods. Parts of the system can be grouped into the following categories:
1. Data acquisition
This phase involves the gathering of different forms of information, such as verbal instructions, electronic messages, user content, and sensors’ outputs. The cloud-computing system, rooted in the given framework, supplies the infrastructure for the storage and maintenance of vast amounts of information collected from a variety of sources.
2. Understanding the Data
Following the gathering of information, the system moves into the processing phase, wherein the information goes through a series of operations such as cleansing, structuring, and analysis. For instance, when the user gives the command, “Play the piece of music,” the system will have to decipher this as the user’s request for a musical piece to be played.
3. Implications of the Findings
The use of artificial intelligence improves the recognition of patterns in historical information, for instance, the individual musical tastes or the standard intervals between users reconfiguring their thermostats. This boost allows the decision-making processes in the system to be better informed and of better caliber in the long run.
4. Decision-Making Process
The system acts according to predetermined logical rules or learned behavior patterns in order to decide on what it will do next. It has the ability to recommend a movie, place a replenishment order, or inform a person.
5. Meeting of User Expectations
Finally, the system has the ability to produce a response that can be delivered through voice, text, or by performing a custom action. Usually, the response is personalized based on the information the system has learned about the user.
Why Cloud Computing Matters
Without cloud computing, intelligent systems wouldn’t work well at scale. Here’s why the cloud is so important:
Flexibility: Cloud infrastructure offers the ability for the system to scale based on changing demand levels. When there are several users accessing the system at the same time, the cloud infrastructure makes resources available on demand.
Velocity: Data centers in the cloud are distributed over a vast geographic area and have increased operational capabilities, allowing for instant and consistent responsiveness.
Storage: Cloud platforms store huge amounts of data, which is necessary for training AI systems. Cloud services are hosted in multiple locations around the world and can be accessed through a vast range of devices.
Maintenance: The tasks of the developer do not include server updating and maintenance, since these are the responsibilities of the cloud service provider.
How AI Powers Intelligence
Artificial intelligence improves the capabilities of systems with the addition of reasoning, learning, and optimization capabilities, resulting in increased operational effectiveness. Some of the most common uses of artificial intelligence include
Experiential learning implies that artificial intelligence algorithms improve continually as they absorb growing amounts of data over a period.
Understanding language: NLP enables the ability of systems to understand and respond to inputs given in the form of human language. Visual and Auditory Recognition: AI can recognize human faces, objects, and auditory signals.
Predictive analysis has the ability to forecast many possible outcomes; for instance, it can assess the probability of a delayed shipment or determine the product most apt to interest a customer next. Generative artificial intelligence provides the machines with the ability to generate written content, create programming languages, as well as musical and visual forms of art.
See also: A Chatbot Without Personalization Has No Purpose. Here’s Why!
Voice-Activated Assistant Technology: An Analytic Framework
Let us consider, for instance, a particular intelligent system, that is, a voice assistant embedded in several smart speaker systems.
Someone asks, “What are the prevailing weather conditions in Seattle?”
The device picks up acoustic waves and sends them to an external server. Cloud computing infrastructure then transcribes verbal exchanges into written form. Understand the importance of the statement. Provides the correct meteorological information.
Gives an audio output, “The weather in Seattle is currently clear skies with a temperature of 72 degrees Fahrenheit.”
Figure 1: High-level voice assistance system architecture
The diagram provides a general overview of the overall infrastructure of a cloud-intelligent voice assistant system and the way a simple voice query—”Hey Alexa, what’s the weather in Seattle?”—may be transformed into a natural and understandable response through the usage of cloud and artificial intelligence technologies.
On the user’s side, the voice interface appears as the user session’s starting point. The user speaks a natural language voice command into a smart speaker or, ideally, a display. There exists in the smart device a microphone as well as a processor, and therefore it can listen for sound and remain in a listening state for some predetermined wake word, in this instance, the name “Alexa.” Upon hearing the wake word, the device starts recording the user’s command and transmits the sound file over a secure path to the cloud servers for processing.
The real intelligence happens in the cloud-processing section illustrated on the right side of the diagram. Automatic Speech Recognition or ASR, in the first pipeline stage in the cloud, transforms the raw audio stream into text. It’s a lot more complicated than it appears; it has to listen for different accents, eliminate background noise, and hear the subtlety of voice in real-time. ASR models are trained on massive sets of recordings of many different voices in order to capture the words spoken as precisely as possible and translate them into text form, “How’s the weather in Seattle?”
Subsequent to this, the system progresses to the Natural Language Understanding (NLU) phase, in which the intent of the user gets deciphered. Converting the user’s voice into text is not enough; the assistant also needs to comprehend the intent of the query and the information or the entities mentioned, i.e., the city “Seattle.” Artificial intelligence models trained in natural language processing are engaged in comprehending the user’s intent in asking for the weather in “Seattle” as the desired city. The assistant also needs to take into consideration the vagueness of the words, the synonyms, and the context for the request to be interpreted in the correct manner.
After the system has gained a full understanding of the query, it moves into the Knowledge and Reasoning phase. Now, in this phase, the assistant selects the manner in which it will access correct weather information, most naturally by asking an authoritative external service, i.e., a weather database or API. The system applies learned algorithms or established behavior in processing information received; for example, it can conclude it’s currently daytime in Seattle and construct a response based on this inference. If the user asks the query, “How’s the weather?”, the system can use geolocation information or past preferences stored in it in order to infer “Seattle” as the most likely location.
Having gathered the required information, the assistant moves into the Natural Language Generation (NLG) stage, in which information in a structured form gets translated into grammatically correct, readable text in an acceptable form for humans. The raw information, for example, {temperature: 72°F, condition: sunny} gets rewritten into, “The Seattle weather is 72 degrees and sunny.” The system can use the correct phraseology, follow grammatical requirements, and add nice variation in the sentence with every repetition in an attempt to make it as natural as possible. Before a response can be released for the user, the system also makes use of the Text-to-Speech (TTS) technology in an attempt to convert the resulting text into sound form. This involves the creation of audible output, normally with a given voice profile of friendly conversation and good enunciation. The audio file afterward gets streamed over the internet with the view of providing the user with a clear, natural-sounding response within seconds of the original statement’s issuance.
The technology basis for this experience lies in cloud technologies. Cloud supports the voice assistant’s scalability with use, in the sense that it can manage millions of simultaneous requests from anywhere in the world. It provides the computational power required to run deep learning algorithms for speech and language, query external databases, and respond in real time. It provides security features such as encrypted transport of data and access controls, such that sensitive voice information is treated with utmost caution. This intelligent system also improves with experience because it learns over time. It can learn from past interactions with the users in order to make it more personalized, learn the users’ preferences, and practically make recommendations prior to the user asking for them.
Conclusion
Intelligent Cloud and AI solutions allow enterprises to go way beyond automation. Intelligent systems listen, learn, and act, thereby creating real value in healthcare, finance, and manufacturing, as well as in day-to-day lives.
Cloud helps these solutions with the power, flexibility, and range they need. Artificial intelligence gives the intelligence necessary for thinking, learning, and optimization. Combined, they are building a smarter, highly interconnected global environment—one system at a time.