Since the start of this year, new development in the field of technology has been the hottest topic of discussion at several science symposiums. This blog post sheds some light on what can be expected for 2017, based on 2016 evolutions in Data Science and Machine Learning.
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Deep Learning Beyond Cats
The year 2012 witnessed how Google discovered cats on the internet through deep neural networks. Since then, in the last 4 years, we have progressed a lot technically, thanks to better nuanced reporting! In 2016, Google’s WaveNet spawned human speech, General Adversarial Networks (GANs) and Plug & Play Generative Networks, while PixelCNN produced images of regular scenes, including objects and animals. Amidst all this, machine translation got better off to a great extent.
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In 2017, though neural networks have found more applications in the industry, adoption may tend to be a tad difficult. The use of Algorithms is on the rise, as games like GO have become undeniably popular. However, laying your hands on the data is the real bottleneck. As a result, extensive work is being carried out on deep neural network to improve architecture, and this year neural network will expand its scopes beyond game playing – it’s time to be hopeful.
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The mushrooming of data roles
As a whole, the domain of data science is evolving. And this year, the proliferation of data roles is on the rise. But the crux of the story remains in the intricateness of new, revamped algorithms, which calls for deep expertise.
Companies and individuals, both take some time to understand the real meaning of all these new job titles – machine learning engineer, deep learning specialist or data science maestro – companies may advertise for data science roles, where in the reality, they need machine learning specialists. Therefore, the main goal here is to understand the difference between the job titles, so that they can work together productively to reap maximum benefits.
Chatbots – failures drive success
2016 was widely famous for being the year of the bots, but this was not the case (sigh). Though the narrative was appealing, the results were not up to the mark. Lowdown on the part of enabling technologies, inefficiency in the distribution of avenues and designing challenges are some of the most potent reasons for failure – hence the bot community learnt some really significant lessons in 2016.
Post soaring expectations throughout 2016, in 2017 we are finally seeing some goal-specific, relevant and narrow-domain chatbots equipped with use case appropriate personality propped up by human agents. Sophisticated intent recognition, swift error handling and diversity in the expansive human-written template responses makes way for more advanced chatbots in the coming years.
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Proper use of data is crucial
Models developed on Facebook profiles to discern job performance, models to frame law enforcements and models to determine recidivism – impact other people’s lives. The data scientists who develop these models may make mistakes with the data, as a result of which some qualified candidate may not make it to his/her dream job.
In 2016, Cathy O’Neill published Weapons of Math Destruction, which dealt with the harmful effects of algorithm – post which, the EU issues a new regulation which gave individuals the right to control their data, while restricting the automation system of decision-making, especially when the algorithm fails to explain the decision to the individual.
This year too, we will make progress in understanding model behavior by getting a hold on algorithms and exercise more control and caution, while framing predictive models in the fields of education, law enforcement and health care.
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