The Machine Learning Landscape

MachineIntelligenceLandscape

Shivon Zilis:

Most of these machine intelligence startups take well-worn machine intelligence techniques, some more than a decade old, and apply them to new data sets and workflows. It’s still true that big companies, with their massive data sets and contact with their customers, have inherent advantages—though startups are finding a way to enter.

Achieving autonomy

In last year’s roundup, the focus was almost exclusively on machine intelligence in the virtual world. This time we’re seeing it in the physical world, in the many flavors of autonomous systems: self-driving cars, autopilot drones, robots that can perform dynamic tasks without every action being hard coded. It’s still very early days—most of these systems are just barely useful, though we expect that to change quickly.

These physical systems are emerging because they meld many now-maturing research avenues in machine intelligence. Computer vision, the combination of deep learning and reinforcement learning, natural language interfaces, and question-answering systems are all building blocks to make a physical system autonomous and interactive. Building these autonomous systems today is as much about integrating these methods as inventing new ones.

The new (in)human touch

The virtual world is becoming more autonomous, too. Virtual agents, sometimes called bots, use conversational interfaces (think of Her, without the charm). Some of these virtual agents are entirely automated, others are a “human-in-the-loop” system, where algorithms take “machine-like” subtasks and a human adds creativity or execution. (In some, the human is training the bot while she or he works.) The user interacts with the system by either typing in natural language or speaking, and the agent responds in kind.

These services sometimes give customers confusing experiences, like mine the other day when I needed to contact customer service about my cell phone. I didn’t want to talk to anyone, so I opted for online chat. It was the most “human” customer service experience of my life, so weirdly perfect I found myself wondering whether I was chatting with a person, a bot, or some hybrid. Then I wondered if it even mattered. I had a fantastic experience and my issue was resolved. I felt gratitude to whatever it was on the other end, even if it was a bot.

On one hand, these agents can act utterly professional, helping us with customer support, research, project management, scheduling, and e-commerce transactions. On the other hand, they can be quite personal and maybe we are getting closer to Her — with Microsoft’s romantic chatbot Xiaoice, automated emotional support is already here.

As these technologies warm up, they could transform new areas like education, psychiatry, and elder care, working alongside human beings to close the gap in care for students, patients, and the elderly.

Advertisements

About GilPress

I launched the Big Data conversation; writing, research, marketing services; https://whatsthebigdata.com/ & http://infostory.com/
This entry was posted in AI, Machine Learning, Uncategorized. Bookmark the permalink.

2 Responses to The Machine Learning Landscape

  1. Pingback: 2 – The Machine Learning Landscape

  2. Pingback: 2 – The Machine Learning Landscape – explodingads

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s