Forrester’s Top Emerging Technologies To Watch: 2017-2021

Forrester’s Top Emerging #Technologies To Watch: 2017-2021
#AI #VR #IoT #bots #cloud

  • Our list focuses on those technologies that will have the biggest business impact in the next five years.
  • We organize this year’s list into three groups – systems of engagement technologies will help you become customer-led, systems of insight technologies will help you become insights-driven, and supporting technologies will help you become fast and connected.
  • Intelligent agents coupled with AI/cogntive technologies will automate engagement and solve tasks.
  • The technology gives firms visibility into and control of customer and operational realities.
  • Hybrid wireless technology will eventually ereate connected cverything.

Forrester: As a refresh to my 2014 blog and report, here are the next 15 emerging technologies Forrester thinks you need to follow closely. We organize this year’s list into three groups — systems of engagement technologies will help you become customer-led, systems of insight technologies will help you become insights-driven, and supporting technologies will…
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Using Machine Learning to Name Malware

Using #MachineLearning To Name #Malware. #BigData #DataScience #AI #Cybersecurity

  • All of the post-processing to settle on one common name has already been done and you can find the library that can guess the virus names at this github repo .
  • items() if v} print(“We have to guess the family name in the following result:\n”) print(to_guess) l_of_l = get_list_of_token_lists([to_guess]) m = tfidf.transform(l_of_l) els_to_pos = {e: tfidf.vocabulary_[e] for e in l_of_l[0]} els_to_scores = {k: m[:, v].
  • “We have to guess the family name in the following result:” {‘AVG’: ‘MLoader’, ‘Ad-Aware’: ‘Gen:Application.
  • Using Machine Learning to Name Malware
  • Let’s create some training data to label parts of virus names with their corresponding tags.

Extracting information from malware names using Conditional Random Fields.
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From big data to human-level artificial intelligence

From #BigData to human-level artificial intelligence #AI |

  • Doug Cutting, Tom White, and Ben Lorica explore Hadoop’s role over the coming decade.
  • Watch the full version of this keynote on Safari .
  • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners.
  • How sensors, fast networks, AI, and distributed computing are affecting the data landscape
  • This is a keynote highlight from Strata + Hadoop World in New York 2016.

What explains the gap between what machines do well and what people do well? And what needs to happen before machines can match the flexibility and power of human cognition?
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Practical data science: Building Minimum Viable Models

Practical #DataScience - Building Minimum Viable Models:  #abdsc #BigData #MachineLearning

  • The working methodology based on minimum and effective models, minimizes the risks in the event the product does not succeed in the market and is an obstacle less in regards to the launching.
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  • For the startups based on data (nowadays, most of them consider their data as a strategic active for the decision making), to find a model that interprets them is a difficult task.
  • It is no use the specialist working only two months with the data, since finding the MVM requires to pay attention to what data provide.
  • MVM is based on the principle that data-based startups need to have affordable data science models for their financial reality but also, these models have to be acceptable in terms of accuracy.

When we talk about innovative services or products, many startups follow a smoother model of development. This allows them to minimize the risk to be able to…
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