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How AI Fits In The Enterprise: 10 Lessons From The Trenches | Chris Mohritz |

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Uploaded on May 16, 2017

Chris Mohritz, 10xEffect says that he has been using AI for last several years both on the start-up side and the enterprise side. He shares insights on things he’s learnt in the last several years. He is an entrepreneur and he helps other entrepreneurs, start-ups, existing or new companies.

His focus is technology. His primary perspective is software based start-ups.

He discusses 10 lessons learned while implementing AI into business environment.

AI basically makes non-smart stuff smart by giving it some intelligence decision making and some human intelligence functions which we take for granted in our mind adding that in non-smart devices today.

In the 20th century we automated work. We converted electrical power, steam power, into work. Now we are doing it through intelligence.

AI is just software which is taking the next step of evolution of software. He will use machine learning and AI in the same context in the discussion. As software has gone more complex over the years there has been a need to move towards more machine learning type of programming model where we can no longer hard code or handwrite software rules. It is now done through data which is machine learning.

Marc Andreessen famously said, “Software is eating the world”.

Now, “AI is eating the software.”

AI is making software smarter. Everything that mankind has invented is up for grabs. Everything will be reinvented smart because AI will be integrated into everything out there. So, it is a very exciting time from an opportunity perspective.

Generally, AI is trying to recreate human brain into everything. To stay smart, you can focus on AI that is task specific.

A great quote entering a popular guy into space. “Anything a human being can do in less than a second, AI can automate today.

Instead of one big system to do everything, think of small systems. Today there is a talk of micro services versus a lot of different small systems.

AI applications can be narrowed with software and these are all API’s available today. Most of these are Microsoft API s.

What you really need is the right technical expertise to cut through the hype of new offerings and the right acumen to know where best to apply those technologies.

It’s easier than you think

You can choose between commercial maturity platform vs an in-house platform

In the case of commercial maturity steps, you can use commercial API with their training data. You can just plug them and go.

Point 3 Hold off on New Teams

A lot of commercial platforms allow you to use your own data for training. You can use cloud and use your own data.

Point 2 Develop in-house

You would be looking at a dedicated team developing an open source platform instead of using your own trading data.

All cloud platform providers have machine learning AI, APIs you can use today. Most of them have a free tier which you can use. You can test it out. It’s faster than you think. This is one of the key points-its faster than you think.

If you are not using AI for your business you will be left behind because AI provides amplification to existing staff, skills, it’s really hard to close the gap.

Quantum Computing: Its architecture is completely different from your regular PCs. It’s much faster. It is suited for the complex type of processing which is aligned with AI type of system. Most of these are seen in Cloud.

After 10 years, there will be nothing but quantum computing. Machine learning running at quantum computing so not only everything is happening faster from a technological breakthrough but there is a potential of taking a major leap forward and the capability of processing as well.

What are your opportunities

Technology is moving so quickly that if you wait for it to work properly or some other reason, you may miss it when you actually need it.

Point 7 Break it into categories

A business can be broken into
1. Marketing
2. Support
3. Product
4. Administration
5. Innovation

Key Takeaways:

The speaker feels that AI should stand for Augmented Intelligence. This is because it is not about one AI that solves all problems but specific AI systems that solve very specific problems, that’s augmenting people.

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