5 trends in AI for 2019

It´s not easy to project trends in a market evolving as rapidly as AI. However, through analysis of cross-industry data and experience with a diverse client-base, we’re willing to make some bets. From automating mundane daily tasks to leveraging computer vision for more accurate medical diagnoses, here are 5 trends in AI we expect to emerge in 2019. 

TREND 1: “EDGY” AI 

Edge AI refers to processing AI algorithms locally instead of relying on cloud services or data centers. 

Smartphones, cars, and wearable devices are examples of devices that need to make faster and more accurate real-time decisions. Autonomous vehicles, for instance, need to make hundreds of decisions per second – brake, accelerate, turn on lights, identify and interpret traffic patterns, signals, and speed limits – all while simultaneously responding to the driver’s voice commands. These decisions must take place in a fraction of a second, and they need to be independent of the connectivity issues that come with cloud computing.  This means that autonomous vehicles need powerful chips to process all this information rapidly and accurately.  

Tech leaders like Nvidia, Qualcomm, Apple, AMD, and ARM are investing in developing and delivering chips that can handle these kinds of workloads. 

In 2019 we’ll see more models being deployed at the edge as well as specialized chips allowing AI models to operate independently from the centralized cloud, or on the “edge” if you prefer.

TREND 2: AI IN HEALTHCARE 

 Last year the FDA (U.S. Food and Drug Administration) approved IDx-DR, an AI-enabled software that can independently diagnose diabetic retinopathy before severe complications (such as blindness) emerge.  

The FDA also cleared Dip.io, a product developed by startup Healthy.io, as a class II medical device. This diagnostic tool can monitor urinary tract infections and track pregnancy-related complications by analyzing photos of dipstick urine tests. It’s as simple as uploading a photo, the model takes it from there.   

2019 will be a remarkable year for AI in healthcare. 

TREND 3: PREDICTIVE MAINTENANCE 

Equipment failure is one of the main causes of production downtime, a huge line-item for any asset-intensive business. However, today maintenance teams spend 80% of their time collecting data but only 20% analyzing it.  

Factory and field equipment generate mountains of unleveraged data that could go a long way to solving these issues. Alongside cameras and sensors, ML-driven algorithms can learn to check assets’ “vital signs,” catch small irregularities (a loose screw) before they turn into larger ones (a damaged turbine) and provide productivity predictions, allowing firms to plan accordingly.      

With sensors becoming more affordable, and edge computing gaining momentum, machine learning will become even more heavily incorporated in industrial processes in 2019. 

TREND 4: CONVERSATIONAL AI 

We say conversational AI, what pops into your head?  If it’s chatbots, you’re not alone. While that’s certainly a huge part, the technology is much broader as it is integrated across messaging apps and voice-enabled virtual assistants who go far beyond the scope of chatbots.    

In 2019 we can expect to see even more AI deployed to handle routine customer service interactions. Whether you’re booking a flight, searching for a new restaurant or requesting the arrival date of your next purchase, AI can assist you.   

Research from eMarketer shows that this year 66.6 million Americans are expected to use speech or voice recognition technology. Banking and retail are great examples of industries already using conversational AI initiatives, and as the technology continues to mature in 2019, we expect to see even more use cases in even more industries.

TREND 5: RPA / BACK OFFICE AUTOMATION 

RPA (Robot Process Automation) covers a variety of back-office tasks that can be automated by bots. It’s not a new concept, nor is it AI. But here are some interesting facts:   

  • According to McKinsey, RPA will have an economic impact of around $6.7 trillion by 2025.   
  • Forrester Research also mentioned that RPA market is estimated to grow to $2.1 billion by 2021.  

Although RPA is not considered AI – since it’s rule-based and can’t learn anything on its own – there’s been a collaboration between RPA and AI.  Due to its capacity of automating repetitive and time-consuming tasks, RPA can save employees tons of time, at the same time it can ensure processes are running smoothly and precisely. On the other hand, AI can enhance RPA.  

For instance, take a bank that’s onboarding a new client and needs to adhere to Know Your Customer/Anti Money Laundering Compliance Regulation. RPA is great for doing a lot of manual work. What AI can do is analyze the data the RPA’s pull in a more sophisticated manner, and arm a Compliance officer with more useful information.  

Whether there is a need to automate processes or implement solutions in this field, RPA has been mainly leveraged by large companies – until now. In 2019 we can expect to see small and medium-size businesses starting to adopt RPA, due to its clear benefits and increased popularity.  

100 most promising AI startups

日本語版はこちら

We’ve come a long way since forming in 2015. Starting out as a small team, we now have four offices worldwide – Lisbon, Porto, Tokyo, and Seattle – and continue to grow every day.  

Our unique platform has helped many successful companies feed their artificial intelligence applications with training data. Using human intelligence coupled with machine-learning, we deliver project-specific, quality-guaranteed data.    

Today, we’re proud to announce that DefinedCrowd is among CB Insights’ third annual list of 100 AI startups. A research team from CB Insights selected 100 startups based on the following factors: investor profile, market potential, partnerships, competitive landscape, and team strength. 

Source: CB Insights

Companies are categorized by focus area. These focus areas aren’t mutually exclusive and include core sectors such as telecommunications, government, retail, healthcare and enterprise tech sectors such as training data (where we sit), software development, data management, and cybersecurity. 

We are pleased to be among this group of incredible AI startups, selected from an extensive list of 3k+ AI companies, and look forward to seeing these companies grow.  

It´s been a great start to 2019. And, we´re very thankful to everyone who has helped get us here.  

AI分野における最も有望なスタートアップ企業 トップ100社

English version available here

DefinedCrowd社の”AI向け学習データプラットフォーム”は、ヒューマンインテリジェンスとマシンラーニングを組み合わせたワークフローにより、、AIアプリケーションの開発・改善に必要な”学習データ”を、お客様毎に、更には、個別のプロジェクト毎に最適化された形でご提供しています。

この度、DefinedCrowd社は、米調査会社CB Insightsが発表した、AI分野における最も有望なスタートアップ企業 トップ100社の中の1社に選ばれました。       

Source: CB Insights

この発表は今年で3回目となり、今回は総数3,000社以上のAI関連のスタートアップ企業の中から、投資家のプロフィール、市場ポテンシャル、パートナーシップ、競争環境、その企業の強みなど、複数の要素を加味・評価した上で、トップ100社が選ばれています。

これらの”AIスタートアップ企業 トップ100社”は、その注力する業界や技術分野毎にカテゴリー分けされており、DefinedCrowd社は”エンタープライズ テクノロジー”の一角、「トレーニングデータ部門」の1社として選ばれました。私たちのデータプラットフォームは、多くの活躍している企業の人工知能アプリケーションに必要なトレーニングデータを、ヒューマンインテリジェンスと機械学習を組み合わせ、プロジェクト固有の高品質データを提供しています。