Had Hergé been into businesses, this article would have been a point of interest to him. And he could have avoided the Big Data errors in his ‘Tintin’ enterprise.

Are you willing to follow down the line too? Not yet intrigued? #Sigh# Thought so! Let’s get you hooked!

Here’s the modern business landscape – Data data everywhere, not a drop to waste! Data has become considerably crucial for modern businesses. In this age even AI is getting powered by Big Data . The secret lies in the capability to collect, sort through, and collate data from diverse sources,This brings in the capability to increase the insight-level and make data-based decisions that enhance business enablement. The leverages extend from marketing, internal workflow to sales for businesses.

Now, where does Big Data come into the business realm? Let’s get to the root of this, shall we?

Owing to modern technologies, all trades, irrespective of size, have access to granular and rich data that is based on their operations and clients. The major hurdle in this is dealing with a massive quantity of data that are both challenging to maintain and costly to manage. Despite the presence of appropriate tools, dealing with such data is a cumbersome activity.

Errors are a frequent presence with the layers of complexity involved in dealing with Big Data. However, Big Data holds diverse leverages for businesses. This includes –

Thus, Big Data becomes the defining leverage for innovative enterprises to gain an edge over their competitors. The usage of these data is sure to exceed 274.3 billion by 2022 globally with each individual generating approximately 1.7 megabytes of information per second .

With such leverages in point, can you really afford to make mistakes with regard to blunders regarding Big Data? So, here are some big data blunders that businesses need to avoid to harness its full capabilities and enjoy the leverages that it brings.

While Big Data comes with highs, lows with errors in the same are not uncommon. The big data problems comprise –

-in case of errors. So, let’s say, Big Data is like credit cards – use them well and they reap benefits; use them unwisely, and the bills are humongous! Here are the listed blunders that businesses should avoid while dealing with Big Data. Read on!

Issue: It seems the ‘look before you leap’ policy is still unknown to many businesses who jump into the initiatives of Big data with colossal data collection. Stalled projects and paralysis of analysis are sure-shot consequences of issues in big data analytics.

Solution: Start on the world of Big Data with ‘small steps’ a.k.a small data quantity. Let your collected data refute or support your hypothesis. In case of ambiguous data, pair it up!

Issue: Security is the first aspect sacrificed when working with Big Data. But what about the mitigating security concerns regarding it?

Solution: There needs to be a multifaceted approach for securing Big Data. This should comprise an understanding of the data possessed, auditing the manipulations of data, and holding control over the privileged users. Make sure to cover the big-data security with a holistic and unified system of processes and controls.

Issue: Complaint about data accuracy and quality are a common occurrence. However, businesses fail to take a look to the roots of this. Lacking central oversight on data collection leads to duplications, incorrect use of columns, horrifying inputs, etc.

Solution: Assign a committee that holds responsibility for data hygiene for your enterprise. Make sure to compel the big data management team to dust up the data and train the users with it.

Issue: Big Data is a colossal jigsaw puzzle that if hastened to solve, it would be a huge jumble. No organization is capable enough to tackle such a huge puzzle.

Solution: Work the puzzle area by area or rather piece by piece. This will make your big data challenges, a small-data one. Enterprises are then equipped enough to tackle such challenges. This definitely eases the job, right?

Issue: Collecting and storing Bitcoins may be of advantage, but it isn’t the way out with data. So, this is a shout out to companies doing this – if you are merely collecting data and not extracting its essence, and implementing the insights, then the silo meditation won’t be of any help. Its power to enhance operation or resolve obstacles, and inform about your product road map, gets rusted.

Solution: Use and extract its essence in time, what else! Don’t let it meditate or go dormant!

Issue: Businesses with smaller datasets are often inclined to get into big data solutions. This quick jump means considerable investment for complex tools that exerts budgetary strain for enterprises.

Solution: Organizations should be hailing their data analysis to lead on with wise-decisions with Big- Data handling. However, not all issues require the use of heavyweight tools. ‘Big Data’ traditional approaches would do!

Apart from the 6 major blunders, there are also the issues of an absent workflow-management tool, out-of-focus ROI, data not being used for evolution, etc.

Big Data is going to be an echo all across 2020 and beyond for all businesses, irrespective of the genre. For experts and developers, this is clearly both an opportunity and a  challenge. As data volumes increase, they will continue to migrate to the cloud and as per predictions, the global data sphere will soon reach 175 zettabytes by 2025 . The increased popularity of machine learning, enhanced demand of Chief Data Officers (CDOs) and data scientists, privacy remaining a consistent concern, and actionable and fast data coming to the front will all add up to Big Data becoming a prominent presence.

Such prosperity of Big Data will have a lot to offer for your organization! Are you willing to pass up or mess with that? We thought so!

DeepMind’s protein-mapping breakthrough is awesome, but it sure as heck isn’t free

DeepMind, the UK-based, Google sister-company, has released a groundbreaking map of human protein structures developed with its breakthrough AI systems.

That’s right, the company that taught AI to defeat humanity’s greatest chess and Go masters has turned its attentions to the greater good. And scientifically speaking, this is really good.

What is protein-mapping and what do board games have to do with any of this?

It’s really complex, but here’s the gist: proteins are the worker bees inside living cells. They do all sorts of stuff from maintaining your fluid balance to repairing damaged tissue and fighting disease.

In short, proteins are responsible for nearly every biochemical reaction that occurs in all living things. Mapping them is incredibly useful, because it allows us to figure out what they do.

Board games have nothing to do with this, but DeepMind is an AI company that most people associate with chess and Go because it used those games to develop the logic behind its AI processes.

DeepMind was founded to tackle some of the biggest open challenges in technology and science. Chess and Go were just means to an end. It was never about the board games, it was always about creating an artificial intelligence capable of augmenting human researchers.

What is protein-folding and why should I care?

It’s exactly what it sounds like. How a protein is formed determines its function. So a protein folded a specific way might have one biological effect whereas one folded differently would do something else.

There are hundreds of thousands of proteins in the human body. And each one can potentially be folded in so many different ways that there isn’t even a word for the number it would take to describe how many potential folds there are.

In other words: It would take almost forever (in the literal sense) for humans to figure out all the folds in all the proteins.

DeepMind’s AI can speed up this process exponentially, and that’s huge. Understanding how proteins work could revolutionize disease fighting, drug discovery, and the field of medical diagnosis. It could also lead to breakthroughs in thousands of other fields ranging from waste management (using enzymes to breakdown trash) to rocketry.

So what did DeepMind actually do?

Figuring out all the potential protein structures isn’t just difficult because of the sheer numbers. We can’t look at proteins under a microscope so we’re forced to examine them via other scientific observations.

DeepMind figured out a way to predict what folded proteins might look like, thus saving researchers from having to painstakingly decode each individual protein.

Imagine your job is to look for a needle in a haystack, but you have a near-infinite number of haystacks and only one has the particular needle you’re looking for. DeepMind developed an AI that helps you figure out where to start and it’s so accurate that most people are going to find the right needle on the first try.

As The Verge‘s James Vincent pointed out earlier today :

Vincent’s article highlights the plight of one research team that spent a decade trying to decode a single protein – something DeepMind’s system did in a matter of minutes.

So what’s your problem? Isn’t this good?

It is good! In fact, it’s freaking awesome. This could very well be the most important scientific breakthrough of the decade, especially if you judge by overall potential impact.

I’ll ask again then, what the heck is your problem?

The short version is that I believe Alphabet (the company that “owns” Google and DeepMind) is placing the entire population of Earth under a scientific conservatorship under no authority but its own.

A conservatorship is when a government awards custody and control of an adult to another adult, allegedly for the good of the first adult. Pop culture aficionados will recognize this as the situation Britney Spears is currently fighting to get out of.

Obviously Alphabet isn’t a government, so I’m using to the term as an analogy for our current reality.

And that reality is this: Alphabet runs DeepMind at a loss. That means that DeepMind would not have accomplished this feat without Alphabet’s money. It would have gone bankrupt getting here.

So it’s definitely true that Alphabet accelerated DeepMind’s research.

Hold that thought for a second, while we revisit Vincent’s article:

McGuffin’s point is an important one: this breakthrough isn’t something only DeepMind could have accomplished, it’s something only DeepMind could have accomplished so quickly.

And how is that bad!?! For the love of Britney, spit it out dude!

Sundar Pichai (the CEO of Alphabet and Google) is acting like Batman. He’s using his companies’ massive coffers to pluck the brightest minds from academia, purchase the most promising research startups, and beat everyone else to the punch when it comes to taking breakthrough research on the cusp of realization to the finish line.

And, like Batman, Sundar Pichai is gaining access to technology that no-one else has. Sure, we’ve got boomerangs and we’ve got AI, but we don’t have Batarangs that can self-track numerous objects and return to a point of release in real-time. We could definitely have that technology in a year or two if someone spent a few billion dollars developing it. But, in reality, Batarangs are a stupid idea.

DeepMind’s AI systems are not a stupid idea. But they’re also not unique. DeepMind picked up where other researchers left off in protein mapping and finished the job. Not because the other researchers weren’t smart enough or talented enough, but because it had virtually limitless funding.

Pichai gets to decide what research gets advanced, which researchers remain in the public sector, and which companies get to exist.

He gets to decide when to monetize the work he’s acquired through hiring and startup buyouts, and when to release that work free to public researchers as part of Alphabet’s hearts and minds campaign .

If Pichai wants to develop a warp drive, for example, he will. He’ll snatch the world’s greatest physicists, buy up any company that’s seriously working on the project, and absolutely ruin the funding opportunities for any researchers who can’t be bought, won’t work for the private-sector, or have some other moral reason for not bending knee to Alphabet.

The entire global population of scientists are, for the first time in history, no longer the leading force for scientific advancement. The shareholders at Alphabet are.

Okay, I see your point. But what does it mean? What’s the worst that could happen?

It might seem hard to imagine what the ramifications of allowing a single corporation to dictate the future of an entire field of human endeavor are, but (un)luckily for us we have a Prime example already.

Ahem. I said… we have a “Prime” example.

We get it. It’s just not funny. You’re talking about Amazon.

Exactly. Remember when Jeff Bezos was just some middle-aged-looking balding guy who put a bookstore on the internet? Amazon posted $389 billion in profits last year. Imagine how many small businesses had to go bankrupt for that to happen.

Now apply that to academia. Eventually, the world’s governments will get tired of wasting billions on public research only to have Alphabet swoop in and snatch victory from the jaws of victory. That funding is going to dry up.

Then if we were very, very lucky we’d be faced with funding research via non-profits, universities themselves, and private donors. But that’s not going to happen. Because the further entrenched Google becomes in the research ecosystem, the more blood in the water there’s going to be for the other big tech sharks.

Today’s breakthrough is a truly wonderful thing. The science community deserves a round of applause for this accomplishment. DeepMind’s open-source protein mapping system will change the world for the better in a lot of ways.

And it’s awesome that Alphabet decided to make it open-source.

But it’s not free. We’re all going to pay, one way or another.

New AI project captures Jane Austen’s thoughts on social media

Have you ever wanted to pick the brains of Sir Isaac Newton, Mary Shelley, or Benjamin Franklin? Well now you can (kinda), thanks to a new experiment by magician and novelist Andrew Mayne .

The project — called AI|Writer — uses OpenAI’s new text generator API to create simulated conversations with virtual historical figures. The system first works out the purpose of the message and the intended recipient by searching for patterns in the text. It then uses the API‘s internal knowledge of that person to guess how they would respond in their written voice.

The digitized characters can answer questions about their work, explain scientific theories, or offer their opinions. For example, Marie Curie gave a lesson on radiation , H.G. Wells revealed his inspiration for The Time Machine , while Alfred Hitchcock compared Christopher Nolan’s Interstellar to Stanley Kubrick’s 2001 .

Mayne also used the system for creative inspiration. When Edgar Allan Poe was asked to complete a poem that started with “ The grey cat stared at the wall and the sounds beyond…” he made the following suggestion:

AI|Writer’s strengths and weaknesses

The characters could also compare their own eras with the present day. When Jane Austen was asked how her characters would use social media in the 21st century, the author replied:

Mayne says the characters did well with historical facts, but could be “quite erratic with matters of opinion” and “rarely reply to the same question in the same way.”

He demonstrated these variations by asking both Newton and Gottfried Leibniz who invented calculus.

“Newton almost always insists that he invented Calculus alone and is pretty brusque about it,” Mayne wrote on his website. “Leibniz sometimes says he did. Other times he’ll be vague.” At one point, Leibniz even threatened to kill Mayne if he tried to take the credit for the discovery.

As well as historical figures, the system can respond in the voice of fictional characters. In fact, Mayne says the most “touching” message he’s received was this reply from the Incredible Hulk.

Mayne is keen to stress that AI|Writer “should not be considered a reliable source nor an accurate reflection of their actual views and opinions” and is merely an experiment to examine interactions with AI. He also plans to open up access to the tool to more people. If you wanna join the waiting list, you can sign up here .