diff --git a/.travis.yml b/.travis.yml
index 162163d9a..01108940d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -6,8 +6,8 @@ python:
   - "2.7"
   - "3.6"
 env:
-  - DEPS="pip nose future numpy scipy"
-  - DEPS="pip nose future numpy"
+  - DEPS="pip nose future numpy scipy dask[array]"
+  - DEPS="pip nose future numpy dask[array]"
 before_install:
   - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
       wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh;
@@ -21,6 +21,6 @@ before_install:
 install:
   - conda install --yes python=$TRAVIS_PYTHON_VERSION $DEPS
   - pip install -v .
-script: 
+script:
   - cd tests      # Run from inside tests directory to make sure Autograd has
   - nosetests     # fully installed.
diff --git a/autograd/core.py b/autograd/core.py
index 77a041ada..fa148d143 100644
--- a/autograd/core.py
+++ b/autograd/core.py
@@ -1,7 +1,7 @@
 from itertools import count
 from functools import reduce
 from .tracer import trace, primitive, toposort, Node, Box, isbox, getval
-from .util import func, subval
+from .util import func, subval, typeof
 
 # -------------------- reverse mode --------------------
 
@@ -230,7 +230,7 @@ def register(cls, value_type, vspace_maker=None):
 
 def vspace(value):
     try:
-        return VSpace.mappings[type(value)](value)
+        return VSpace.mappings[typeof(value)](value)
     except KeyError:
         if isbox(value):
             return vspace(getval(value))
diff --git a/autograd/numpy/numpy_vspaces.py b/autograd/numpy/numpy_vspaces.py
index 8eda1b2a2..6bcd096c3 100644
--- a/autograd/numpy/numpy_vspaces.py
+++ b/autograd/numpy/numpy_vspaces.py
@@ -3,7 +3,8 @@
 
 class ArrayVSpace(VSpace):
     def __init__(self, value):
-        value = np.array(value, copy=False)
+        if not hasattr(value, 'shape') or not hasattr(value, 'dtype'):
+            value = np.array(value, copy=False)
         self.shape = value.shape
         self.dtype = value.dtype
 
diff --git a/autograd/tracer.py b/autograd/tracer.py
index 3dfc7fd84..8dffea589 100644
--- a/autograd/tracer.py
+++ b/autograd/tracer.py
@@ -1,9 +1,11 @@
 import warnings
 from contextlib import contextmanager
 from collections import defaultdict
-from .util import subvals, toposort
+from .util import subvals, toposort, typeof
 from .wrap_util import wraps
 
+import numpy
+
 def trace(start_node, fun, x):
     with trace_stack.new_trace() as t:
         start_box = new_box(x, t, start_node)
@@ -115,7 +117,7 @@ def register(cls, value_type):
 box_type_mappings = Box.type_mappings
 def new_box(value, trace, node):
     try:
-        return box_type_mappings[type(value)](value, trace, node)
+        return box_type_mappings[typeof(value)](value, trace, node)
     except KeyError:
         raise TypeError("Can't differentiate w.r.t. type {}".format(type(value)))
 
diff --git a/autograd/util.py b/autograd/util.py
index 4cfd7a369..f20f8e078 100644
--- a/autograd/util.py
+++ b/autograd/util.py
@@ -38,6 +38,20 @@ def toposort(end_node, parents=operator.attrgetter('parents')):
             else:
                 child_counts[parent] -= 1
 
+
+def typeof(x):
+    """
+    A Modified type function that returns np.ndarray for any array-like
+
+    This improves portability of autograd to other projects that might support
+    the numpy API, despite not being exactly numpy.
+    """
+    if all(hasattr(x, attr) for attr in ['__array_ufunc__', 'shape', 'dtype']):
+        import numpy
+        return numpy.ndarray
+    else:
+        return type(x)
+
 # -------------------- deprecation warnings -----------------------
 
 import warnings
diff --git a/tests/test_numpy_like.py b/tests/test_numpy_like.py
new file mode 100644
index 000000000..ad023920e
--- /dev/null
+++ b/tests/test_numpy_like.py
@@ -0,0 +1,28 @@
+from __future__ import absolute_import
+import warnings
+
+import autograd.numpy as np
+import autograd.numpy.random as npr
+from autograd.test_util import check_grads
+from autograd import grad
+from numpy_utils import combo_check
+
+from dask.array.utils import assert_eq
+import dask.array as da
+
+npr.seed(1)
+
+def test_dask():
+    x = np.arange(10)
+    xx = da.arange(10, chunks=(5,))
+
+    assert_eq(x, xx)
+
+    def f(x):
+        return np.sin(x).sum()
+
+    f_prime = grad(f)
+
+    assert isinstance(f_prime(xx), type(xx))
+
+    assert_eq(f_prime(x), f_prime(xx))