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Why Neural Networks & Machine Learning Equals “Perspective Intelligence” Today

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There is no doubt that automation and machines are increasingly playing a pivotal role in making our lives more convenient and simplified. What started with something as basic as using machines such as a keyboard has now morphed into voice- and visual-enabled ‘smart’ assistants (think: chatbots, robots, digital agents, etc.).

One of the reasons behind this drastic transformation is the consistent use of ‘intuitive intelligence’ that allows these tools to automatically learn – and adapt – from past experiences and valuable human input. 

Let’s look at what the data tells us:

  • “The global machine learning market is expected to reach $20.83B by 2024, growing at a CAGR of 44.06% between 2017 and 2024.”
  • According to research by Tractica, “The annual global AI software revenue will grow from $10.1B in 2018 to $126.0B by 2025.”
  • One in ten enterprises now use ten or more AI applications.”

Clearly, Artificial Intelligence and Machine Learning are becoming more mainstream with every new product/service innovation. Let’s deep-dive to understand this better. Are you ready?

The Role of Machine Learning, Deep Learning, & Artificial Intelligence in Our Life

By 2025, the global AI market is expected to be almost $60 billion.”

Believe it or not, machines today are being built on one central premise: To replicate human behavior for menial as well as advanced tasks. In fact, deep neural networks can now translate speech-to-text, caption photographs, and even translate languages at unimaginable speeds and scale.

While most agree that machines cannot replace humans in the foreseeable future, they do play a bigger (and more productive) role in our lives – From asking Siri to book an appointment to using hand gesturing and facial recognition capabilities on our iPhones, we’re constantly engaging with a device that can perceive our needs and wants; and fulfill them.

Long story short, AI is ushering in a new era of digital machines that can simulate a human brain and offer unparalleled convenience and benefits to the users. Here’s what Amit Ray from Compassionate Artificial Intelligence has to say about deep learning – a subset of machine learning:

“Incorporating general intelligence, bodily intelligence, emotional intelligence, spiritual intelligence, political intelligence, and social intelligence in AI systems are part of the future deep learning research.”

How AI & ML is Driving “Perceptive Intelligence”

At this point, you might be wondering how ‘inanimate’ machine-led devices can successfully work in the right context and interact with users in an intelligent and optimal capacity? Logically speaking, most of the interactions and inputs that are fed into these systems are human-based. Plus, these devices make use of intelligent systems such as neural networks, machine learning, etc. to sense and perceive like us humans.

The devices use visual and audio sensors to recognize a human’s wants, needs, and preferences. In short, what we are left with is ‘Perceptive Intelligence’ that feeds on inter-connected data streaming in from a large ecosystem of devices – that’s consistently learning from each other.

Key takeaway: Perceptive intelligence devices today don’t even require a connection to the internet. Newer localized systems such as edge-based processing can run sophisticated AI and machine learning algorithms in a local, offline capacity. This not only lowers costs for the enterprise but also provides a secure environment to assess video and audio data and interact intelligently in real-time – a priceless advantage!

The Future of Internet of Things (IoT), AI, & Perceptive Intelligence 

Granted that the term IoT came into existence in 1999; however, this technology gained traction only in 2011. Undoubtedly, since then, its application and effectiveness has skyrocketed over the years. To truly get a grasp of how IoT is penetrating markets and industries at scale, consider the following numbers: 

  • Every second, 127 new IoT devices are connected to the web.
  • By 2021, 35 billion IoT devices will be installed worldwide.
  • By 2025, more than 75 billion IoT devices will be connected to the web. – Source

Now, it doesn’t take a Mathematician to add up the numbers and make sense of it. IoT is the future of digital transformation – connecting physical spaces with digital ones. 

That said, there’s one major difference between technologies such as AI and IoT: The latter leverages diverse sensors, devices, and other emerging technologies that don’t directly interact with consumers as opposed to the former where direct customer interaction takes center stage.

Bonus material: If you’re someone who loves podcasts, listen to this podcast by Rashmi Misra from Microsoft, where she talks about how AI and IoT are collectively offering increased visibility and control of devices that are connected to the internet.

Key takeaway: IoT-enabled devices and sensors, along with AI and machine learning capabilities, are giving rise to a collaborative and interconnected business environment – one where business relationships are centered around user-friendly customer outcomes and product offerings are based on data- and insight-driven innovation. All in all, companies can extract and transform data into practical, valuable information, thanks to IoT’s prowess and its dynamic capabilities.

Top-4 Real-Life Brands that are Setting Serious ‘Perceptive’ Intelligence Goals

1. Voice-enabled systems: Voice-assistants are no longer following a “Your wish is my command…” type philosophy. They’ve increasingly become context and conversationally aware. Take a look at purely ML-based voice assistant “Siri,” which uses deep neural networks to imitate human conversations and preempt user needs by analyzing data.

2. Facebook Feed: Here’s what Kevin Lee, a technical program manager at Facebook, has to say:

“If you’ve logged into Facebook, it’s very likely you’ve used some type of AI system we’ve been developing.”

That’s the extent to which Facebook – the largest social media network globally – utilizes AI. Ever wondered how your Facebook feed ‘determines’ what kind of content you’d like to see? Surprise, surprise, it’s because AI is at play. Specific algorithms are developed into the feeds that filter content so that users get relevant posts.

That’s not all. According to recent reports, Facebook can help prevent suicides through the use of AI by flagging key phrases in the posts.

3. Netflix:

75% of Netflix users select films recommended to them by the company’s machine learning algorithms.” – Forbes

Netflix offers personalized movie recommendations using AI, data, and Machine Learning. Here’s the famous “Because you watched” section that you must have experienced while using the platform.

This is just the beginning. The platform uses ML to auto-generate and customize thumbnails based on user preferences. Netflix ranks each image to hand-pick thumbnails that may have the highest click-through-rates.

4. Uber:

“Uber uses machine learning for ride ETAs, estimated meal delivery times for UberEATS, computing optimal pickup locations, as well as for fraud detection.” – Danny Lange, Uber’s Head of Machine Learning 

American multinational ride-hailing company, Uber makes excellent use of Machine Learning to offer a variety of services to users. From determining the price of the car ride to minimizing wait-time/detours for users.

In Closing, Let’s Recap…

All things said and done, AI and ML are intertwined technologies that are working in conjunction to deliver perceptive and seamless user experiences, keeping data and past experiences in ‘mind.’ What’s important is that enterprises need to work on feedback and implement changes to their business plan as a modification in strategies with every user interaction. The more the real-time iterations, the better your machine will be at ‘perceiving’ your customer’s needs. Here’s a quick recap of the blog:

  • Artificial Intelligence helps build neural networks that can imitate human intelligence and behavior, such as logical reasoning, self-correction, etc.
  • Machines, by themselves, cannot simulate a human brain. They need real-time, valuable data at hand. This is where IoT comes into the picture.
  • Machine Learning, a subset of AI, allows a machine/computer system to learn from its environment and extract insights from varying sources of data – without requiring in-depth coding.

Due to the inherent ‘learning-centric’ nature and complexity of these emerging technologies, device-based ‘perceptive intelligence’ is fast becoming the norm. In sum, AI, ML, and deep learning are more ubiquitous than you’d think. Needless to say, what was considered to be “Sci-Fi” yesterday, is now an active part of today’s reality. What do you think?

About the author


Laduram Vishnoi

Laduram Vishnoi is CEO and Founder at Acquire. He loves to share his research and development on Artificial intelligence, machine learning, neural network and deep learning.