New job – training eagles to bring down drones!

eagle attacks drones

We already are witnessing the adoption of drones in all kinds of tasks across industries. I have been wondering about the jobs that this is going to create in future. And this I have been wondering about quite a few kind of jobs which do not exist today or atleast people do not even imagine but they are every much going to be there in very near future – reason why I think this is something I should start posting about.

Today I came across this post which talks about how Eagles are destroying drones because they see them invading their territories. Here is the link to article: Birds are fighting back for their territory already

Just one example quoted in above article talks about how a wedge-taled eagle destroyed a $80,000 drone in a matter of seconds!

And it ends with how French are using golden eagles to destroy the drones operated by terrorists.

Which opens up a whole new job category – people who will be able to train eagles (or maybe other birds) in messing up with or destroying the drones. Going further I can even imagine that one trains another bird to launch a counter attack on the destructive bird to protect the drone. The job definitely looks interesting and will require people who can really ‘tame’ and train these birds to achieve the defined ‘goals’.

#JobsOfTheFuture

Data breach at Quora!

Earlier today an email from Quora landed in my inbox informing about a data breach.

The mail says:

The following information of yours may have been compromised:

  • Account and user information, e.g. name, email, IP, user ID, encrypted password, user account settings, personalization data
  • Public actions and content including drafts, e.g. questions, answers, comments, blog posts, upvotes
  • Data imported from linked networks when authorized by you, e.g. contacts, demographic information, interests, access tokens (now invalidated)
  • Non-public actions, e.g. answer requests, downvotes, thanks
  • Non-public content, e.g. direct messages, suggested edits
  • Account and user information, e.g. name, email, IP, user ID, encrypted password, user account settings, personalization dataPublic actions and content including drafts, e.g. questions, answers, comments, blog posts, upvotesData imported from linked networks when authorized by you, e.g. contacts, demographic information, interests, access tokens (now invalidated)Non-public actions, e.g. answer requests, downvotes, thanksNon-public content, e.g. direct messages, suggested edits

  • Public actions and content including drafts, e.g. questions, answers, comments, blog posts, upvotesData imported from linked networks when authorized by you, e.g. contacts, demographic information, interests, access tokens (now invalidated)Non-public actions, e.g. answer requests, downvotes, thanksNon-public content, e.g. direct messages, suggested edits

Had high expectations from Quora but this shows almost nobody is taking security seriously probably because there seems to be no real incentive. Security breaches now seem to be the new normal !

AI in Drug Development

Recently I had the opportunity to speak at DataHack Summit 2018 , which is undoubtedly the best event in India now when it comes to analytics and AI. It was organised by Analytics Vidhya which is a fantastic platform for anybody interested in Artificial Intelligence, Machine Learning, Deep Learning or Analytics in general. There were amazing speakers and 1300+ attendees across industries, experience and regions.
My session was about AI in Drug Development where I took few examples where we at Innoplexus are using AI to help global pharmaceutical companies in reducing the time and effort across the different stages of drug development.
Here are my slides – itself made using AI ofcourse 😉
Gaurav Tripathi with Kunal Jain, CEO of Analytics Vidhya

From “Data is Oil” to “AI is Electricity” : A decade of evolution

Data is Oil, AI is new electricity
A decade of evolution.

This one slide sums up a decade (2006-2016) of evolution in the areas of data processing, analysis, computing, machine learning and artificial intelligence.

Clive Humby, UK Mathematician and architect of Tesco’s Clubcard, said in 2006 (widely credited as the first to coin the phrase): “Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”

Andrew Ng gave the phrase “AI is the new Electricity” in 2016. “Electricity changed how the world operated. It upended transportation, manufacturing, agriculture, health care. AI is poised to have a similar impact” he said. Information technology, web search, and advertising are already being powered by artificial intelligence. It decides whether we’re approved for a bank loan. It helps us order a pizza and estimate our wait time, and even tells the driver where to deliver it. Other areas ripe for AI impact: fintech, logistics, health care, security, and transportation.

“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years,” Ng says.

Few pictures from beautiful Panchgani

Crossing over the mountain

The lone tree

entry to the cave

Just below the table top in morning

Half way while running on take top in evening

Mesmerizing sunset

Low light picture trial

Scary ?

Colors of evening

AI bots as managers ?

If we remove the ability to have an emotional connect and being able to form meaningful bonds then we can as well hire AI bots to lead us, don’t really need a human leader. At the end I believe AI is going to make us more human by forcing us to focus on traits which define us as a human, something which no AI can manage (yet). #AI #Leadership

This article by Rasmus Hougaard, Jacqueline Carter and Vince Brewerton in Harvard Business Review reminds managers that they are human beings.

Why Do So Many Managers Forget They’re Human Beings?

There is another old article from HBR which touches upon the subject of having robots as managers. It kinds of explores the subject with both pros and cons of having a robot as a manager.

In the end it simply depends on people – there are those who have seen abusive managers and leaders and are ready to give bots a chance to manage assuming it will still be better than what they have faced already while there are those who had an opportunity to work with really good humans as their managers and leaders who will never accept AI bots as their managers. Maybe, a time will come when everybody will have to make a choice between the two.

 

 

Capturing sunset

These pictures are from the terrace of our Innoplexus office building Midas Tower.

Doesn’t matter how many times I go there and look at the open sky and the amazing view, it always looks new to me. For quite some time I have been thinking about capturing the sun set – had to wait for the perfect day with clear visibility, without any fog or haze to get these shots.

Innovation eats data for breakfast

Data Driven Innovation

I read this amazing article from Prof. Viktor Mayer-Schönberger and Thomas Ramge on Harvard Business Review. Follow the link here – https://hbr.org/2018/02/are-the-most-innovative-companies-just-the-ones-with-the-most-data

They have made an excellent case for a data driven innovation. Citing examples of how Google, Apple and Amazon are using data to further innovation and outsmarting not just their existing competitors but also the startups, they have made it very clear that the future innovation will be owned by enterprises which are able to leverage AI in generating insights from the data and not just rely purely on human ingenuity.

I am sharing here the two adjustments, they mentioned about, that enterprises need to make:

First, they need to reposition themselves in the data value chain to gain and secure data access. Second, as innovation moves from human insight to data-driven machine learning, firms need to reorganize their internal innovation culture, emphasizing machine learning opportunities and putting in place data exploitation processes.

Now, one interesting point to ponder about is – what do we mean by data really ? In all of their examples it means the data generated by users which is being used by large corporations to generate insights for improving their products or services.

What does it mean for companies whose product or services are not consumed or delivered online ? eg. Pharmaceuticals

In that case they need to look at the data that is publicly available. Thankfully, there is a lot of data that is available publicly eg. information on clinical trials, research publications, disclosures to regulatory bodies, reviews from regulatory bodies, data from different scientific congresses across the world, theses from universities, patents from major patent bodies globally, data from major global regulatory bodies etc.

In the context of the second adjustment above, enterprises need to leverage AI to automate not just the collection and curation of data but also the generation of insights, specific to key processes, from the data. Entire Drug Development process is very long and complex, with a lot of tasks / processes which are repetitive in nature or involve a lot of manual content reviews at each step. It is up to the enterprises to empower their teams to step up the value chain from doing manual analysis of data to being the domain specialists who can partner with Data Scientists to come up with good training data sets, to validate the output of these algorithms and to be the data quality supervisors.

In the end, it will only improve the overall satisfaction of employees as they will free to solve real problems instead of clerical tasks, which ultimately leads to happier and more productive workplace.

P.S. The title (Innovation eats data for breakfast) is just a pun on the famous “Culture eats strategy for breakfast”, a phrase coined by the legendary Peter Drucker.

How to improve search on HBR.org ?

ZERO results

The goal for any content driven website is to provide its users with relevant content based on Information Discovery. Most of the websites choose ‘search’ as a tool for information discovery (Netflix is a mind blowing exception to this) and in search, relevance is heavily dependent on context rather than just strings present in them. Not many websites realise this and I was surprised to see that HBR.org , known for having one of the best content repositories,  giving barely useful results for my search queries.

Now, I am a happy subscriber of Harvard Business Review and have been using it often to enhance my limited knowledge by discovering interesting content around technology, business, leadership etc. and have absolutely no doubt on the high quality of its content and really great contributors. But, I am disappointed at how that content is becoming difficult to discover in the first place. Here is how its search looks like:

:hbr.org search

All looks well, it has a  very familiar interface, like most of the other sites have and the first result does have the word I searched for “search”. Don’t go away yet, stay with me till the end 🙂

In my quest to learn more and follow everything about Artificial Intelligence I keep looking everywhere for anything related to AI. I thought let’s try what all is there in HBR.org repository on AI. I search first for artificial intelligence and look at the results:

First result is a “Sales and Marketing” case study about an early stage company Empathetics (an organization that teaches empathy to healthcare professionals and staff to improve the patient experience) and I wonder why is that the top result (when sorted by relevance!) for what I searched for. I open it, scratch my head really hard to figure out what exactly is related to AI there but could not find anything. I scroll down and to my despair, the other results are also out of the world for me. Here they are:

and

Then I thought of trying the ‘exactness’ trick – I searched for “Artificial Intelligence” and boom!

ZERO results

ZERO results! Apparently it does not support exact string search, which ideally it should. I knew this is simply not true as HBR.org does have articles on AI. Examples:

https://hbr.org/2018/01/is-your-companys-data-actually-valuable-in-the-ai-era

https://hbr.org/2018/01/the-5-things-your-ai-unit-needs-to-do

https://hbr.org/2018/01/robo-advisers-are-coming-to-consulting-and-corporate-strategy

It gives irrelevant results in search even though the right content is very much there in repository.

Moving on, I tried taking my chances on AI – I search for ‘AI’. Here is what happens:

Do you notice it? There is an author Ai-Ling Jamila Malone whose name contains “AI” and HBR.org simply is showing me all articles from the author. This means it is giving a higher score (probably) to words found in a wrong field (author field)  than the content itself and that too without any context.

Now it could have been deliberately done assuming most of the people want to search for names of authors but hey, that use case CAN be handled in a better way.

Moving further, I check for another hot topic – Deep Learning – and default (sorted by relevance) results appear to be relevant (see now it shows me results for artificial intelligence as well).

But the articles are old and I needed the latest ones – the moment I try sorting by publication date I see this:

the first result is this – https://hbr.org/2018/01/the-5-things-your-ai-unit-needs-to-do – which may seem somewhat relevant given it has AI in its title but it is not even remotely related to “Deep Learning” and the only reason it appears in search results is because there is a word “deep” in one paragraph somewhere

and there is another paragraph with the word “learning” somewhere.

The other results are same –

https://hbr.org/2018/01/why-people-really-quit-their-jobs

https://hbr.org/2018/01/you-dont-just-need-one-leadership-voice-you-need-many

https://hbr.org/product/ch%C3%A2teau-margaux-launching-the-third-wine-abridged/518070-PDF-ENG  — this tops the chart of weirdness for me.

they have the words “deep” and “learning” somewhere and hence it is being shown. An important point to consider – I want latest but still relevant results and HBR.org fails at it. It does not identify ‘deep learning’ as a concept made of two words and is simply looking up the words appearing somewhere in the content.

The root causes for all of it can be summarised as follows:

  1. The search at HBR.org is still relying on basic keyword based scoring and has no Ontology of concepts like “Artificial Intelligence” or “Deep Learning” or the relationships between the concepts.
  2. It does not account for synonyms and hence is unable to understand that “Artificial Intelligence” and “AI” are same concepts. An Ontology makes it much easier to maintain all synonyms of any given concept.
  3. It does not identify entities so is unable to differentiate between name of a person “Ai-Ling Jamila Malone” and a concept “AI”

Based on how we have designed information discovery for our Data as a Service (DaaS) platform iPlexus.ai I can say that the primary reason for all of the problems is the missing Ontology leading to missing Entity Recognition and disambiguation.

Search is important but in itself is not always the best way for information discovery. Users don’t get happy at seeing millions of search results for what they search – that only adds to information overload. What matters is, if you are telling me there are so many possible results out there, then tell me how they are distributed across different dimensions around my ‘interest’. Let me choose a direction and don’t force me to keep going through all the results in a linear fashion – nobody will live through to get to the end of the millionth result page. And not just Innoplexus but I know there are few other companies out there who are following this philosophy and making Information Discovery easier for their users. One of the examples I quoted above as well is Netflix.

True information discovery tool has to be a Digital Gyroscope helping one to explore the Data Universe by giving a sense of all possible directions and saving one from getting lost in the hyperspace.

Thanks to Ravi Ranjan for reviewing the article and helping with the headline 🙂